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Foundations and Trends R Information RetrievalVol. 4, No. 5 (2010) 377–486  2011 C. Castillo and B. D. DavisonDOI: 10.1561/1500000021 Adversarial Web Search
By Carlos Castillo and Brian D. Davison Search Engine Spam Activists, Marketers, Optimizers, and Spammers The Battleground for Search Engine Rankings Previous Surveys and Taxonomies Overview of Search Engine Spam Detection
Editorial Assessment of Spam Feature Extraction Dealing with Content Spam and
Plagiarized Content

Types of Content Spamming Content Spam Detection Methods Malicious Mirroring and Near-Duplicates Cloaking and Redirection E-mail Spam Detection Curbing Nepotistic Linking
Link-Based Ranking Link Farm Detection Combining Links and Text Propagating Trust and Distrust
Trust as a Directed Graph Positive and Negative Trust Propagating Trust: TrustRank and Variants Propagating Distrust: BadRank and Variants Considering In-Links as well as Out-Links Considering Authorship as well as Contents Propagating Trust in Other Settings Detecting Spam in Usage Data
Usage Analysis for Ranking Spamming Usage Signals Usage Analysis to Detect Spam Fighting Spam in User-Generated Content
User-Generated Content Platforms Publicly-Writable Pages Social Networks and Social Media Sites The (Ongoing) Struggle Between SearchEngines and Spammers Research Resources Foundations and Trends R Information RetrievalVol. 4, No. 5 (2010) 377–486  2011 C. Castillo and B. D. DavisonDOI: 10.1561/1500000021 Adversarial Web Search
Carlos Castillo1 and Brian D. Davison2
1 Yahoo! Research, Diagonal 177, 8th Floor, Barcelona 08018, 2 Lehigh University, 19 Memorial Drive West, Bethlehem, PA 18015, USA, Web search engines have become indispensable tools for finding content.
As the popularity of the Web has increased, the efforts to exploit theWeb for commercial, social, or political advantage have grown, makingit harder for search engines to discriminate between truthful signals ofcontent quality and deceptive attempts to game search engines' rank-ings. This problem is further complicated by the open nature of theWeb, which allows anyone to write and publish anything, and by thefact that search engines must analyze ever-growing numbers of Webpages. Moreover, increasing expectations of users, who over time relyon Web search for information needs related to more aspects of theirlives, further deepen the need for search engines to develop effectivecounter-measures against deception.
In this monograph, we consider the effects of the adversarial rela- tionship between search systems and those who wish to manipulatethem, a field known as "Adversarial Information Retrieval". We showthat search engine spammers create false content and misleading linksto lure unsuspecting visitors to pages filled with advertisements or mal-ware. We also examine work over the past decade or so that aims to discover such spamming activities to get spam pages removed or theireffect on the quality of the results reduced.
Research in Adversarial Information Retrieval has been evolving over time, and currently continues both in traditional areas (e.g., linkspam) and newer areas, such as click fraud and spam in social media,demonstrating that this conflict is far from over.
Information Retrieval (IR) is a branch of computer science that dealswith tasks such as gathering, indexing, filtering, retrieving, and rankingcontent from a large collection of information-bearing items. It is a fieldof study that is over 40 years old, and started with the goal of helpingusers locate information items in carefully curated collections, such asthe ones available in libraries. In the mid-1990s, the emergence of theWorld Wide Web created new research opportunities and challenges forinformation retrieval. The Web as a whole is larger, less coherent, moredistributed and more rapidly changing than the previous documentcollections in which IR methods were developed [9].
From the perspective of an information retrieval system such as a search engine, the Web is a mixture of two types of content: the "closedWeb" and the "open Web" [37]. The closed Web comprises a small num-ber of reputable, high-quality, carefully maintained collections which asearch engine can fully trust. The "open Web", on the other hand,includes the vast majority of Web pages, and in which document qual-ity cannot be taken for granted. The openness of the Web has beenthe key to its rapid growth and success, but the same openness is themost challenging aspect when designing effective Web-scale informationretrieval systems.
Adversarial Information Retrieval addresses the same tasks as Infor- mation Retrieval: gathering, indexing, filtering, retrieving, and rankinginformation, with the difference that it performs these tasks in collec-tions wherein a subset has been manipulated maliciously [73]. On theWeb, the predominant form of such manipulation is "search enginespamming" (also known as spamdexing or Web spam). Search enginespamming is the malicious attempt to influence the outcome of rankingalgorithms, usually aimed at getting an undeservedly high ranking forone or more Web pages [92].
Among the specific topics related to Adversarial Information Retrieval on the Web, we find the following. First, there are severalforms of general Web spam including link spam, content spam, cloak-ing, etc. Second, there are specialized forms of Web spam for particularsubsets of the Web, including for instance blog spam (splogs), opin-ion spam, comment spam, referrer spam, etc. Third, there are ways inwhich a content publisher may attempt to deceive a Web advertiser oradvertiser broker/intermediary, including search spam and click spam.
Fourth, there are other areas in which the interests of the designersof different Web systems collide, such as in the reverse engineering ofranking methods, the design of content filters for ads or for Web pages,or the development of undetectable automatic crawlers, to name a few.
Search Engine Spam
The Adversarial IR topic that has received the most attention has beensearch engine spam, described by Fetterly et al. as "Web pages that holdno actual informational value, but are created to lure Web searchers tosites that they would otherwise not visit" [74].
Search engines have become indispensable tools for most users [17].
Web spammers try to deceive search engines into showing a lower-quality result with a high ranking. They exploit, and as a result,weaken, the trust relationship between users and search engines [92],and may damage the search engines' reputation. They also make thesearch engine incur extra costs when dealing with documents that havelittle or no relevance for its users; these include network costs for down-loading them, disk costs for storing them, and processing costs for 1.2 Activists, Marketers, Optimizers, and Spammers indexing them. Thus, the costs of Web spam are felt both by end-usersand those providing a service to them.
Ntoulas et al. [182] measured Web spam across top-level domains (TLDs) by randomly sampling pages from each TLD in a large-scaleWeb search engine, and then labeling those pages manually. In theirsamples, 70% of the pages in the .biz domain, 35% of the pages in .usand 20% of the pages in .com were spam. These are uniform randomsamples, while the top results in search engines are much more likelyto be spam as they are the first target of spammers. In a separatestudy, Eiron et al. [69] ranked 100 million pages using PageRank andfound that 11 out of the top 20 achieved such high ranking throughlink manipulation.
Ignoring Web spam is not an option for search engines. According to Henzinger et al. [98], "Spamming has become so prevalent that everycommercial search engine has had to take measures to identify andremove spam. Without such measures, the quality of the rankings suf-fers severely." In other words, on the "open Web", a na¨ıve applicationof ranking methods is no longer an option.
Activists, Marketers, Optimizers, and Spammers
The existence of Web spam pages can be seen as a natural consequenceof the dominant role of search engines as mediators in informationseeking processes [85]. User studies show that search engine users onlyscan and click the top few results for any given search [87], which meansthat Web page exposure and visitor traffic are directly correlated withsearch engine placement. Those who seek visibility need to have pagesin the top positions in search engine results pages, and thus have anincentive to try to distort the ranking method.
There are many reasons for seeking visibility on the Web. Some people (activists) spam search engines to further a political message orto help a non-profit achieve its end. This is the case of most link bombs(perhaps better known as Google bombs) that spam a particular termor phrase to link it to a particular Web page. A memorable example ofthis manipulation is the one that affected the query "miserable failure",which during the 2004 presidential election, returned the home page of George W. Bush as the first result in several Web search engines. Thiswas the result of a coordinated effort by bloggers and Web page authorsaround the world. We discuss link bombing further in Section 4.
Most search engine spam, however, is created for financial gain.
There is a strong economic incentive to find ways to drive traffic to Websites, as more traffic often translates to more revenue [231]. Singhal [212]estimated the amount of money that typical spammers expected toreceive in 2005: a few US dollars per sale for affiliate programs onAmazon or E-Bay, around 6 USD per sale of Viagra, and around 20–40USD per new member of pornographic sites. Given the small per-salecommissions and the low response rates, a spammer needs to collectmillions of page views to remain profitable. Further, some spam pagesexist to promote or even install malware [68, 192, 193].
The incentive to drive traffic to Web sites, both for legitimate and illegitimate purposes, has created a whole industry around searchengines. The objective of Search Engine Marketing (SEM) is to assistmarketers in making their Web content visible to users via a searchengine.1 SEM activities are divided by the two principal kinds of infor-mation displayed on a search results page: the editorial content and theadvertising (or "sponsored search").
Advertising on search engines today is also a ranking process, involv- ing bidding for keywords to match to user queries, the design of theads themselves, and the design of the "landing pages" to which usersare taken after clicking on the ads. An advertiser's goal in sponsoredsearch is to attract more paid traffic that "converts" (i.e., buys a prod-uct or service, or performs some other action desired by the advertiser),within a given advertising budget.
Sponsored search efforts are fairly self-regulated. First, marketers have to pay the search engine for each click on the ads. Second, themarketer does not simply want to attract traffic to his Web site, but toattract traffic that leads to conversions. Thus, it is in his best interestto bid for keywords that represent the actual contents of his Web site.
1 Some practitioners define SEM more narrowly, focusing on the sponsored search side, but from a business perspective, all of these efforts fall under marketing.
1.3 The Battleground for Search Engine Rankings Also ad market designers are careful to design systems that provideincentives for advertisers to bid truthfully.
The objective of Search Engine Optimization (SEO), on the other hand, is to make the pages of a certain Web site rank higher in theeditorial side of search engines, in order to attract more unpaid ororganic traffic to the target Web site.
The efforts of a search engine optimizer, in contrast, are not self- regulating, and in some cases can significantly disrupt search engines, ifcounter-measures are not taken. For this reason, search engines threatenSEOs that have become spammers with penalties, which may includethe demotion or removal from the index of pages that use deceptivepractices. The penalties that search engines apply are well known bythe SEO community. Boundaries are, of course, fuzzy, as all searchengines seem to allow some degree of search engine optimization.
Moran and Hunt [169] advise Web site owners on how to tell search engine spammers from SEOs. A search engine spammer tends to (i) offera guarantee of top rankings, which no reputable firm can do as thereare many variables outside their control; (ii) propose minimal changesto the pages, which indicate that they are likely to create a link farm(described in Section 4.3) instead of actually modifying the way thecontent is presented to users and search engines; and (iii) suggest to useserver-level cloaking (described in Section 3.5) or other modificationswhose typical purpose is to spam.
The Battleground for Search Engine Rankings
In general, search engine results are ranked using a combination oftwo factors: the relevance of the pages to the query, and the authorita-tiveness of the pages themselves, irrespective of the query. These twoaspects are sometimes named respectively dynamic ranking and staticranking, and both have been the subject of extensive studies from theIR community (and discussed in IR textbooks [13, 58, 154]).
Some search engine spammers may be assumed to be knowledgeable about Web information retrieval methods used for ranking pages. Nev-ertheless, when spammers try to manipulate the rankings of a searchengine, they do not know the details about the ranking methods used by the search engine; for instance they do not know which are the spe-cific features used for computing the ranking. Under those conditions,their best strategy is simply to try to game any signal believed to beused for ranking.
In the early days of the Web, search engine spammers manipulated mainly the contents and URLs of the pages, automatically generatingmillions of pages, including incorporating repetitions or variants of cer-tain keywords in which the spammer was interested. Next, as searchengines began to use link-based signals [33, 34, 122, 183], spammersstarted to create pages interlinked deceptively to generate misleadinglink-based ranking signals.
As the search engines adapted to the presence of Web spam by using more sophisticated methods, including the usage of machine-learning-based ranking for Web pages [201], more elements of the pageswere taken into consideration which pushed spammers to become moresophisticated. Next, the possibility of adding comments to forums andthe existence of other world-writable pages such as wikis presented newopportunities for spammers as they allowed the insertion of arbitrarylinks into legitimate pages.
Recently search engines have devised other ways of exploiting the "wisdom of crowds", e.g., through usage data to rank pages, but searchengine spammers can also pose as members of the crowds and disruptrankings as long as they are not detected. Web spam has been evolvingover the years, and will continue to evolve to reflect changes in rankingmethods used by popular services.
Thus, there are a variety of useful signals for ranking and each of them represents an opportunity for spammers, and in Sections 3–7 wewill highlight how spammers have taken advantage of these opportu-nities to manipulate valuable ranking signals and what work has beendone to detect such manipulation.
Previous Surveys and Taxonomies
In 2001, Perkins [189] published one of the earliest taxonomies of Webspam. This taxonomy included content spam, link spam, and cloaking.
It also suggested a test for telling spam from non-spam: Spam is "any 1.5 This Survey attempt to deceive a search engine's relevancy algorithm", non-spamis "anything that would still be done if search engines did not exist, oranything that a search engine has given written permission to do." ongyi and Garcia-Molina [93] proposed a different tax- onomy. This taxonomy stressed the difference between boosting tech-niques and hiding techniques. Boosting techniques are directly aimedat promoting a page or a set of pages by manipulating their contents orlinks. Hiding techniques, instead, are used by spammers to "cover theirtracks", thus preventing the discovery of their boosting techniques.
In 2007, a brief overview of Adversarial IR by Fetterly [73] appeared in ACM Computing Reviews. It included a general description of thefield, and references to key articles, data sources, and books related tothe subject. In the same year Heymann et al. [101] published a surveyfocused on social media sites, stating that in the case of social mediasites, a preventive approach was possible, in addition to detection- anddemotion-based approaches. Prevention is possible because in socialmedia sites there is more control over what users can do; for example,CAPTCHAs can be incorporated to prevent automated actions, therate at which users post content can be limited, and disruptive userscan be detected and banned.
Additionally, several Ph.D. and M.Sc. theses have included elements related to Web spam. A partial list of them includes theses in the areasof link spam [95, 149, 160, 208], splogs and spam in blogs [124, 166],content spam [180], Web spam systems in general [45, 232, 236, 251],and search engine optimization [123].
We have left out the closely related subject of e-mail spam. While some methods overlap, particularly in the case of content-based Web-spam detection (which we discuss in Section 3.6), there are substantialdifferences between the two areas. For a survey on e-mail spam, see,e.g., Cormack [55].
This Survey
In this survey we have tried to be relatively inclusive; this is reflectedin citations to about 250 publications, which we consider large for asurvey on a young sub-field of study. We also intended to appeal to a wide audience including developers and practitioners. For this reason,we have chosen to present general descriptions of Web spam techniquesand counter-measures, and to be selective with the details.
The rest of this monograph is organized as follows: Section 2 describes general systems for detecting search
engine spam, including the choice of a machine learning
method, the feature design, the creation of a training set, and
evaluation methodologies.
Section 3 describes content-based spam techniques and how
to detect them, as well as malicious mirroring, which is a form
of plagiarism for spam purposes.
Section 4 describes link-based spam techniques and how to
detect them, and covers topics such as link alliances and nepo-
tistic linking.
Section 5 describes methods for propagating trust and dis-
trust on the Web, which can be used for demoting spam pages.
Section 6 describes click fraud and other ways of distorting
Web usage data, including Web search logs; it also deals with
the subject of using search logs as part of Web spam detection
Section 7 describes ways of spamming social media sites and
user-generated content in general.
Finally, the discussion in Section 8 includes future research directions
and links to research resources.
Overview of Search Engine Spam Detection
Adversarial Web IR problems can be attacked from many differentperspectives, including Information Retrieval, Machine Learning, andGame Theory. Machine learning methods have been shown to be effec-tive for many document classification tasks and Web spam is not anexception. In this section we briefly outline how an automatic Webspam classifier is usually built; for surveys of approaches for textclassification in general and Web page classification in particular, seeSebastiani [207] and Qi and Davison [195], respectively.
We discuss first how to create a training corpus in Section 2.1, then how to represent documents through features in Section 2.2. Next,we discuss the choice of a learning mechanism in Section 2.3 and theevaluation of a system in Section 2.4.
Editorial Assessment of Spam
Current Web spam classification systems used by search engines requiresome degree of supervision, given that spam techniques may vary exten-sively. Moreover, the difference between spam and non-spam pages canbe the result of very small changes.
Overview of Search Engine Spam Detection The design of a sound labeling procedure for the training instances includes the development of clear guidelines for the editors. This firstnecessitates the operationalization of a definition of spam. For example,given that the Web is comprised of pages, perhaps the pages containinginappropriate material should be marked; or perhaps it is the pagesthat benefit from the inappropriate material (e.g., the page that is thetarget of an inappropriate link) that should be marked; or perhaps itis the material itself (link, content, redirection, etc.) that is marked,and not the pages containing or benefiting at all. Moreover, there is adecision as to the granularity (domain, host, page, or page element) atwhich the label should be applied. For instance, tagging at domain levelversus tagging at site or page level may influence the evaluation of theeffectiveness of spam detection methods, particularly if some hosts of adomain are in the training set and some hosts in the testing set [217].
In their help pages, search engines have various definitions of what constitutes Web spam, emphasizing different aspects. These publicguidelines show only a moderate level of overlap among different searchengines, and they also tend to be terse. The guidelines that are actu-ally used by the editors that work for search engines are not known indetail.
One approach to designing guidelines for the editorial assessment of spam (used in the creation of a few public datasets1) is to enumeratedifferent spamming characteristics that pages may have, and describethem through examples.2 This means framing the task as "finding pageswith spamming aspects", and trying to the best possible extent todecouple the problem of finding spam from the problem of assessingthe quality of Web pages.
It is important that the editors realize that opposite of spam is not high-quality; in theory spam and quality are independent axesand the opposite of spam is simply "non-spam".3 In practice spamand quality tend to be correlated, but apart from the expected low-quality + spam and high-quality + non-spam examples, there is also 2 See for one such set of guidelines and examples.
3 Also referred in the e-mail spam literature as ham.
2.1 Editorial Assessment of Spam low-quality + non-spam and high-quality + spam. For instance, in 2006Google temporarily removed4 both and from its indexafter detecting that these sites, which hold high-quality and legitimatecontent, used deceptive JavaScript-based redirects.
Subjectivity in Assessment
Labeling Web spam is a task involving a great deal of subjectivity.
There are cases in which spam is obvious to a human, and cases wherespam is hard to see. There are many borderline cases, including pagesthat seem to provide utility for users by themselves, but also use recip-rocal linking to distantly related sources that are suspicious of beingspam. There are also cases of unsophisticated spam, such as inten-tional reciprocal linking among family or friends (and general linkexchanges) [62].
In practice the degree of agreement in spam assessment tasks has been reported as either poor (κ = 0.45 in [21]) or moderate (κ = 0.56in [43]). To alleviate this problem, the task must be specified very care-fully to the assessors, making sure that they understand the definitioncorrectly. An alternative to compensate for the low agreement is tocollect many pairs of judgments so that we are likely to find more pairsin which the editors agree (in which case we would perhaps throw awaythe rest).
Some have proposed that more assessments can be collected by using a form of a two-player game [84] similar to the ESP game for labelingimages [227]. In any case, given a small budget of assessments, activelearning can be used. In active learning, a classifier or a set of classifiersis available at the time the assessments are collected. The classifiers runover the whole collection finding items that they cannot classify withhigh confidence, or in which they disagree on the predicted label. Thoseexamples are the ones presented to the editors. This reduces the timerequired of editors, and it has been studied in the context of splogs byKatayama et al. [121].
Overview of Search Engine Spam Detection Hybrid Sites with Both Spam and Non-spam Pages
If the classification is done at the host level, it is important to con-sider that some hosts may supply a mixture of non-spam and spamcontents. Some hosts include publicly writable pages (see Section 7.3)such as Wikis or pages containing comment forms that can be abusedby spammers to insert links to the sites they want to promote.
Other sites are simply compromised by spammers. Data from the closely related area of phishing (a widespread type of e-mail fraud)indicates that the landing pages of the "phishing" messages are hostedin compromised Web sites in 76% of the cases and on free hosting siteson 14% of the cases [168]. The same reference indicates that Web sitesthat are vulnerable to compromise can be easily located through simpleWeb searches, e.g., by searching for names of popular scripts known tobe vulnerable; so easy to locate indeed that 19% of the compromisedphishing sites are re-compromised in the next six months.
This means that in general it is not safe to assume that hosts are either entirely spam or entirely non-spam, and that Web spam classifi-cation should occur at a finer granularity than that of entire hosts.
To be effective, an automatic Web spam classifier needs a rich docu-ment representation that takes many aspects of a page into accountbefore taking a decision. This representation is obtained by collect-ing typically hundreds of features for each page. These features, whiletypically specific to a page, can be divided by their sources.
There are basically three points in time at which features can be computed: while pages are being crawled, while pages are beingindexed, and while pages are being ranked in response to a user's query.
Next we provide an overview of them, and defer the details to thesections dealing with specific types of spam.
Out of the three sources of features, the most important role isplayed by index-time features, which include any features that can be 2.2 Feature Extraction calculated at index-generation time (that is, without knowledge of aparticular query).
are those features that are simply a func- tion of the content of the Web object (e.g., the text on the page).
Content-based spam detection methods are discussed in detail in Section 3, particularly in Section 3.3. One of the most comprehensivestudies of content-based features is due to Ntoulas et al. [182]. Amongtheir findings, they note that many spam pages: (i) have an abnor-mally high number of words in the title, (ii) are either longer or shorterthan non-spam pages, (iii) use words that are longer than average,(iv) contain less HTML markup and more text, and, (v) have moreredundant content as measured by applying a text compression algo-rithm and observing the compression ratio. Spam pages also tend tocontain words that appear in popular queries, among other featuresthat can be exploited by a classifier.
are those features that reflect the existence of hyperlinks between Web pages. These may be locally calculated values(e.g., number of outgoing links) or a global value such as the importanceof the page compared to all pages in the graph.
Link-based spam detection methods are discussed in detail in Sections 4 and 5. Link-based metrics can be used as features for thepages and hosts being analyzed. Section 4.4.2 describes features thatcan be extracted to detect anomalous linking patterns, such as degreecorrelations, neighborhood sizes, etc. Gy¨ ongyi et al.'s TrustRank [94] is an example of a global value which has been used as a feature forspam classification (described in Section 5.3).
Links can also be incorporated directly during the learning process (instead of using them simply to compute features), as explained inSections 2.3.2 and 2.3.3.
are characteristics extracted from records of human interactions with the pages or sites, and include measures suchas the number of times a particular site was visited.
Usage-based spam detection methods are discussed in detail in Section 6. Usage data can be used to detect spam; for instance, browsing Overview of Search Engine Spam Detection trails can be used to find sites in which users spend too little time, orto which users never arrive by following links. These trails can be col-lected, e.g., by a toolbar or add-on supplied by the search engine that,with explicit permission from the user, submits anonymous informationabout the user's activities. This is discussed in Section 6.3.1.
Query sessions collected in search engine logs can be used to identify popular queries (which can help determine if a page is made almostexclusively of popular query terms), or to identify pages that attractvisits from too many unrelated queries. This is discussed in Section 6.3.2 are features that incorporate time or change over time, such as the number of incoming links a page has acquiredthis year.
A recent development has been the consideration of how the Web changes over time and the effect on Web spam. Chung et al. [53] reporton how link farms evolve over time, finding that large link farms werecreated quickly, and that they did not grow. Others [60, 71] asked thequestion of whether historical information could be of value in spamdetection. Dai et al. [60] obtained archival copies of Web pages fromthe Internet Archive, and based on features derived from how the pageschanged in the past, obtained a substantial improvement in classifieraccuracy.
If a Web crawler can discard pages that are almost certainly spam, asearch engine can avoid the costs of storing and indexing them, pro-viding substantial savings. This can be achieved by avoiding crawlertraps, by prioritizing high-quality sites, or by running an automaticspam classifier at the crawler.
Heydon and Najork [100], when documenting their Web crawling system Mercator, describe the presence of crawler traps as early as1999. Crawler traps are programs on Web sites that automatically gen-erate an infinite Web of documents, and many sites that contain anabnormally high number of documents are indeed cases of crawler traps.
One option is to establish a maximum number of pages to download perhost; Lee et al. [140] propose to allocate these maxima proportionally 2.2 Feature Extraction to the number of in-links from different domains received by each hostin the crawler's queue.
URLs sometimes provide enough information to guide the crawling process. Bar-Yossef et al. [16] present a classification scheme to avoiddownloading near-duplicates by identifying different URLs leading tothe same text. Ma et al. [152] propose a spam-specific classifier thatuses URL features, in particular information from the hostname portion(e.g., IP address or geolocation).
Crawlers can also exploit features gathered from the HTTP response to the HTTP request used to fetch a page. Webb et al. [234] observethat when comparing spammers with non-spammers in a large corporafrom the Web, the distribution of Web sites into IP addresses is muchmore skewed for spammers than for non-spammers. This means thatspammers are often concentrated into a few physical hosts. If spam-mers also use the same software in all their hosts, this can be used bysearch engines to increase the effectiveness of features obtained fromthe HTTP response headers while crawling. For instance, these headersmay provide information about the specific server version and moduleversions being used, thus helping identify a group of servers belongingto a single entity.
In general, a scheduling policy for crawling that emphasizes page quality can help stop spam at crawling time; a survey of such policiesappears in [40].
Many pages optimized for search engines actually succeed in foolingsearch engine ranking methods. The amount of spam pages is massive,and for queries that have moderate (neither very high nor very low)frequencies, it may be the case that there is a spam page that is heavilyoptimized for that query. For instance, a page containing all the queryterms in the title and the URL will have a big boost in ranking, evenif textual similarity is just one of many factors of the search engineranking.
Svore et al. [217] suggest to use query-dependent features that are computed after a (preliminary) set of result pages is selected and ranked Overview of Search Engine Spam Detection according to some ranking algorithm. These rank-time features can beused to build a last "line of defense" against spam, and may include forinstance the number of query terms in the title and other sections ofa document, counts of different occurrences of the query terms acrossdocuments, and word n-gram overlaps between the query terms andthe document. The purpose of these features is to find anomalies andremove or demote results that were ranked high by the search enginebut whose statistical properties deviate significantly from the othersearch engine results of the same query.
In this section we present the types of learning schemes used to trainWeb spam detection systems. First, local learning methods considereach node as a separate entity, and independently infer its label (spamor non-spam). Second, neighborhood-based learning methods introduceadditional features for each node, based on the nodes they are linkedto; the process still infers the label of each node independently. Third,graph-wide learning methods compute simultaneously the labels for allof the nodes in a graph, using the links between nodes as dependenciesthat have to be taken into account during the learning process.
Link-based features and content-based features can be used together toclassify each page in isolation based on all the different signals avail-able. Indeed, this was the approach taken in one of the earliest worksin this subject by Davison [62], in which multiple signals were incorpo-rated into a single classifier (albeit to recognize spam links, rather thanspam pages). The classifier used was a C4.5 [197] decision tree. Wanget al. [229] also describe a classifier based on Ranking Support Vec-tor Machines (Ranking SVMs) [99] that ranks pages into three classes(good, normal, and spam) using features from the contents and linksof the pages.
A common concern for these methods is how to perform feature aggregation in the cases in which features are extracted at a differ-ent granularity from the one at which the classification needs to be 2.3 Learning Schemes performed. For instance, one could need to classify entire sites basedon features that include the sizes or number of links of individual pages.
In this case, a possible approach is to compute multiple aggregates fromthe pages of a site, representing each host by features including, e.g.,the average page size, the maximum page size, the average number oflinks, and the maximum number of links.
Non-graphical features can be used in conjunction with the link struc-ture of the Web to create graph-regularized classifiers that exploit "guiltby association"-like rules. Figure 2.1 illustrates that hosts connected bylinks tend to belong to the same class (either both are non-spam or both Fig. 2.1 Host graph from [44] including several thousand nodes from the .uk labeled byeditors. Black nodes are spam sites and white nodes are non-spam sites.
Overview of Search Engine Spam Detection are spam). This correlation can be exploited when learning to detectspam nodes.
One might simply look at the result of classifying neighboring nodes when deciding the class of the current node. Qi and Davison [194]demonstrate this method for topical classification of Web pages.
In stacked graphical learning, this approach can be applied more than once (allowing for propagation beyond immediate neighbors).
A standard classifier is first used to obtain a base prediction for anode. Let k be the number of features used by this classifier. Next, anaggregate (such as the average) of the base predictions of the neigh-bors of a node is used to compute an extra feature. This extra feature isadded to the features of the original node, and then the base classifier istrained again using k + 1 features. For the task of Web spam detection,the resulting classifier has been shown to be more accurate [44]. Thismethod can be applied recursively for a few iterations, stopping whenthe accuracy in the test set no longer improves. Computationally, thisis fast given that in practice the base classifier needs only to be invokeda few times, and the extra feature can be computed quickly.
Another approach is to consider the graph structure directly when stat-ing the objective function for the learning process. The methods thattake into account this type of dependencies are known as graphicallearning methods or more generally as collective inference methods.
At a high-level, these methods operate in a transductive setting, a learning paradigm in which all the test instances (all of the Web pagesindexed by the search engine) are known at training time. This includesthe pages for which we do not have human-provided labels. By knowingthe testing set in advance, the algorithm can produce a classifier thatgenerates "smooth" predictions, that is, predictions that are similar forneighboring nodes. As an example, in Section 4.6, we discuss Abernethyand Chapelle [2] and Abernethy et al.'s [4] study which demonstratesthe potential for graph regularization in Web spam detection.
Finally, not only edges, but also nodes, can be given different weights. Most pages on the Web are seldom visited and never show 2.4 Evaluation up among top results in search engines, and there is a small fractionof pages that are very important for users. This motivates incorporat-ing measures of page and link importance during the learning process.
Zhou et al. [254] describe a method in which the PageRank score forthe nodes in the graph is computed, and then PageRank scores areused to weight nodes and PageRank flows are used to weight edges.
These weights can be used during the learning process; for instanceclassification errors on the top pages can be given more weight thanclassification errors on pages that are not so important.
Given a ground truth consisting of a set of labels for elements knownto be spam or known to be non-spam, for evaluation purposes the set isdivided into a training set, used to create the automatic classifier, anda testing set, used to evaluate it. In this section, we outline some of thecommonly used methods for evaluating Web spam detection systems.
Evaluation of Spam Classification Methods
There are a number of methods for the evaluation of automatic clas-sifiers, see, e.g., [235]. In the context of Web spam detection, given aclassification method and a testing set, we can examine first its confu-sion matrix: where a represents the number of non-spam examples that were cor-rectly classified, b represents the number of non-spam examples thatwere falsely classified as spam, c represents the spam examples thatwere falsely classified as non-spam, and d represents the number ofspam examples that were correctly classified.
For evaluating a classification algorithm, two important metrics are the true positive rate (or recall) and the false positive rate. In a Webspam detection system, the true positive rate R is the amount of spam Overview of Search Engine Spam Detection that is detected (and thus may be deleted or demoted). The false pos-itive rate is the fraction of non-spam objects that are mistakenly con-sidered to be spam by the automatic classifier. The true positive rateR is defined as d . The false positive rate is defined as: b .
The F-measure F (also called F1 score) is a standard way of sum- marizing both aspects in a single number. The F -measure is defined asF = 2 P R , where P is the precision P = d .
P +R Most classification schemes generate a binary prediction (non-spam or spam) based on an estimation of the probability that an object isspam (a "spamicity" score) which is then thresholded to produce thefinal output. The drawback of the F -measure is that it requires a fixedchoice of classification threshold, and the resulting performance can bequite sensitive to that choice.
As a result, it is better to ignore the choice of a threshold, and evaluate instead the ordering of the pages induced by the "spamicity"estimates of an algorithm. The Area Under the ROC curve (AUC) met-ric provides a natural measure of the accuracy of a predicted ranking,and requires only that the algorithm outputs an ordering of the test set.
A good classification algorithm should give higher spam scores to spampages than to non-spam pages; the threshold and the weight given tothe spam score in the final ranking are left as choices for the searchengine designer who uses a spam classifier.
Evaluation of Spam Demotion Methods
Some methods for fighting Web spam are not based on classifica-tion. Instead, they try to modify the way a certain authority estima-tion method is computed in order to produce a different ranking. Forinstance, they might alter the way PageRank counts different links tolessen the effect of link manipulation. In this case, typical evaluationcompares the original ranking ordering with the modified one.
This type of evaluation is applied in several papers introducing spam-aware ranking methods (e.g., [20, 21, 94, 178, 242, 243]). Theelements (pages or hosts) are divided into a set of b buckets. The ele-ments in each bucket are assigned in descending rank order on the basisof the authority score given to each element by the ranking function.
2.4 Evaluation The assignment is such that each bucket contains elements whose scoresadd up to 1/b of the score.
Thus, the last bucket contains the set of smallest valued elements.
The next to last contains the next smallest scores, and so on. The firstbucket contains the highest-scoring pages. If the scores are distributedaccording to a skewed distribution such as a power-law (which is thecase for PageRank), then the first bucket contains very few elementscompared to the last bucket.
At this point, one can then ask about the distribution of subsets of those pages (e.g., where spam pages are located). Proposed spam-awareranking methods would then use buckets of the same sizes and againdistribute pages according to their score. A successful method wouldbe one that tends to push less-desirable pages toward the bottom ofthe ranking (to buckets with low score) and potentially desirable pagestoward the top.
Since this approach implicitly considers the importance of a page (e.g., there is a high cost for permitting a spam page to rank highly), itis arguably an improvement over the simpler methods for spam classi-fication that do not consider ranking positions. However, in most caseswe do not know whether the ordering generated (that also demotesspam) is valuable as an estimate of authority for result ranking. More-over, no single metric for demotion has gained enough acceptance inthe research community to be used for comparison.
In summary, methods that are evaluated based on spam classifica- tion or spam demotion cannot be assumed to reduce directly the impactof spam on retrieval by users.
Evaluation in Retrieval Context
One of the principal reasons to detect search engine spam is to improvethe results seen by a searcher. Thus, much of the research we describeis intended to ameliorate the effect of search engine spam. Joneset al. [117] refer to this as nullification. They also distinguish betweendetection and nullification; the effect of removing all spam pages mightmiss the spam links between "good" pages, or might punish sites thatpermit user submitted content (which we will discuss in Section 7).
Overview of Search Engine Spam Detection approach. Notable exceptions include Davison et al. [176, 177, 240, 241],Jones et al. [116], and very recent work by Cormack et al. [56]. Thisis likely the result of several factors. First, depending on the data setand queries chosen, there may be few instances of spam pages rank-ing highly and thus having an impact on quality metrics. Second, andmore significantly, it requires more resources than spam detection: atminimum a full search engine, indexed data set, and queries, but typ-ically also require (expensive) relevance judgments of the kind used inthe long-running Text Retrieval Competitions (TREC) organized bythe U.S. National Institute of Standards and Technology. Thus, mostof the research that we will describe focus on spam detection.
This section described how to create a Web spam page classificationsystem. We started by examining the problem of acquiring labeled dataand the extraction of features for use in training a classifier. Typicalclassifiers were shown to use the content on a page, the links betweenpages, and even the content or classes of neighboring pages. Finally,in Section 2.4, we saw that most evaluation focused on the quality ofthe classifier, while some work also considered the effects on ranking.
Most of these ideas will be present in the remaining sections, whichgo into additional detail on specific methods for dealing with searchengine spam.
Dealing with Content Spam and
Early search engines such as Webcrawler, Altavista, and Lycos reliedmainly on the content of pages and keywords in URLs to rank searchresults. Modern search engines incorporate many other factors butcontent-based methods continue to be an important part of the compu-tation of relevance. However, page contents and URL keywords can beeasily manipulated by spammers to improve their ranking. The meth-ods that spammers use typically involve repetition or copying of wordsor passages of text that contain keywords that are queried frequentlyby search engine users.
This section is about content-based Web spam, and methods to detect Web spam that are content-based. We start with somebackground on features for static and dynamic ranking. Then wedescribe particular forms of content-based Web spam including mali-cious mirroring and cloaking. While not being the central topic of thismonograph, in the last section we provide a couple of pointers aboute-mail spam detection.
Dealing with Content Spam and Plagiarized Content Modern search engines use a variety of factors to compute theimportance of a document for a query. Richardson et al. [201] indicatethat these factors include similarity of the document to the query, rank-ing scores obtained from hyperlink analysis, page popularity obtainedfrom query click-through logs, and other features about the page, host,or domain itself.
Static and Dynamic Ranking
Query-independent features are also referred to as static ranking fea-tures, and they can be pre-computed at indexing time, thus saving timewhen answering a user's query. Among these features are aspects of thepages such as total document length, frequency of the most frequentword, number of images embedded in the page, ratio of text to markup,and many others. From a search engine's perspective, the effectivenessof a large number of features can be tested using a feature selectionmethod to filter out the irrelevant ones, so in practice the number ofstatic features that are computed can be a few hundreds.
Most of the features for static ranking are under the control of the document author because they depend on aspects of the page itself. Forsearch engines, this means that the specific list of features that have ahigh weight in the ranking function cannot be disclosed or Web siteswill optimize their pages according to those features, rendering themineffective for ranking.
Without knowing exactly why content is considered high quality, one way of leveraging the quality is, for instance, to copy well-writtenarticles from other sources and modify slightly the copies to includespam terms or links. The purpose of such a copy is to create a spampage having static feature values resembling those of a high-qualitypage, with the hope of making it rank highly in search engine results orhoping that the high-quality text will make a spam link more believable.
Dynamic ranking features, on the other hand, are those that can only be calculated at query-time, and most obviously include estimatesof the relevance of the document to the query but can also includeuser-specific features such as the searcher's country, query history, 3.1 Background time-of-day, etc. Given our focus on content in this section, we nextdescribe in more detail how the content can be modeled and how therelevance of a query to a document can be estimated.
Some researchers have performed controlled experiments to deter- mine what factors (dynamic and static) are important to the rankingprocess used by search engines [27, 68, 216]. The methodology used toperform some of these experiments is public and can be reproduced bysome spammers.
The dominant paradigm for determining the relevance of a document to a query is the vector-space model, described by Saltonet al. [204]. The vector-space model is an instance of a "bag-of-words"model in which only the number of occurrences of words in a documentis taken into account, but not the ordering in which they appear.
We sketch here a basic version of the vector-space model; for details see [13]. Both the query q and each document di, i = 1,2,.,N in a
document collection D of size D = N are represented as vectors in
RT , where T is the total number of terms in the collection.
The value of the j-th coordinate of a document vector di indicates
roughly the strength with which document di is associated to term j
and how rare is term j in the collection. This strength is often computed
as a product of a term frequency tfi,j and an inverse document frequency
(di)j  tfi,j × idfj.
The term frequency gives more importance to words appearing mul- tiple times in a document with respect to words appearing less often.
The frequency of term j in document di can be defined as:
k=1 ni,k where ni,j indicates the number of occurrences of term j in document
The inverse document frequency gives more importance to rare words that do not appear in many documents as opposed to words like Dealing with Content Spam and Plagiarized Content "the" which appear everywhere in an English language document col-lection. For a term j, its inverse document frequency can be defined as: idfj  log {di ∈ D : ni,j > 0} ,
that is, the logarithm of the reciprocal of the fraction of documents ofthe collection in which the term j appears.
Using this representation, the relevance of document di for a query q
is defined as the cosine of the respective vectors. There are more elab-orate choices for representing the documents and the query; a popularchoice is Okapi BM25 [203] which follows basically the same principlesbut differs in the specific computation of the coordinate values.
From an adversarial perspective, the document di is under the con-
trol of its author, who has complete control over the number of occur-rences ni,j of any term j in the document and thus has an opportunityto manipulate the ranking function as we show in Section 3.2.
n-Grams and term proximity In the context of information
retrieval, a language model for a document collection is usually under-
stood as its distribution in terms of words or sequences of words. A pop-
ular class of language models are n-gram models. A word n-gram is a
sequence of n-words in order. When there is no possible confusion with
character n-grams, which are sequences of n characters, word n-grams
are simply called n-grams. n-Grams are useful from an information
retrieval perspective as they preserve the ordering of words in a doc-
ument, as opposed to "bag-of-words" models, while keeping the com-
putational requirements low due to the use of a fixed length for the
Rasolofo and Savoy [199] introduce the idea of using term proximity during the ranking process. Documents in which query terms appearclose to each other are given a higher ranking than documents in whichthe query terms are spread across different passages of the document.
This method can be implemented efficiently in practice. The techniqueis improved and evaluated experimentally by B¨ uttcher et al. [39].
Again, from an adversarial perspective, the frequencies of n-grams and the term proximities can be manipulated by the document author,thus opening the possibility of gaming the information retrieval system.
3.2 Types of Content Spamming At the same time, a richer document representation yields more waysof detecting spam pages.
Types of Content Spamming
ongyi and Garcia-Molina [93] introduce a comprehensive taxonomy of content spam. Content spam can be created, according to theirnomenclature, by: • repetition of terms to boost their TF values in TF.IDF • dumping unrelated terms or phrases into the page to make the page at least partially "relevant" for multiple topics; • weaving spam phrases into non-spam content copied from other sources; or • stitching together non-spam content to create new artificial content that might be attractive for search engines.
A different dimension of content spam classification is the location of the spammed content. If the spam content is on the page itself, itcan be either body spam, title spam, or meta-tags spam depending onthe part of the HTML document where the spam is located. The spamcontent can also be located outside the page, for instance in the URL,by creating long URLs or host names with many terms, or in the anchortext of a link farm created to boost the popularity of the target page.
All of these may be sources of signals used by a search engine for queryrelevance assessment.
Content Spam Detection Methods
Document Classification Methods
Ntoulas et al. [182] describe several content-based features, some ofwhich were already mentioned in Section 2.2.1.
Besides differences, e.g., in length, number of words in the title, and other characteristics, they found that spam pages have an abnor-mal language model, including the fact that they contain more popularterms than non-spam pages. This is exploited, e.g., by Chellapilla and Dealing with Content Spam and Plagiarized Content Chickering [50] to detect cloaking pages. Ntoulas et al. also built a3-gram language model for the whole collection and compared it withthe subset of spam and non-spam documents. They found that spampages have abnormally low or abnormally high likelihood given thecollection, basically, because their distribution of n-grams is often sub-stantially different from the background distribution. The likelihoodof a document in this setting is the probability of generating thatdocument by a process of independently drawing n-grams using thelanguage model of the whole collection, until reaching the documentlength.
A richer representation of the textual content of the documents can be used to improve the accuracy in this classification task. For instance,Piskorski et al. [190] experimented with annotating the documents withpart-of-speech (POS) tags that indicate the morphological class of eachword (e.g., adjective, noun, verb, etc.). This leads to features such asPOS n-grams that can indicate, for instance, that a sequence such asnoun,verb,noun is more likely than verb,verb,verb in non-spampages written in English.
Textual features can also include term distance features, as pro- posed by Attenberg and Suel [10]. The proposed method computes thefrequency of pairs of words at a certain distance (lying in a particulardistance bucket); and use this as a feature for classifying documents asspam or non-spam.
Rather than a static content analysis, Zhou et al. [253] propose to calculate the maximum query-specific score that a page with n key-words and l occurrences can achieve, and pages with scores close tothat maximum are considered more likely to be exploiting term spam.
Content-based features obtained at the page level can be aggregated to obtain features for classifying at the level of entire hosts. Fetterlyet al. [74] report three such useful aggregates. The first two are thevariance of word counts of all pages served by a single host and thedistribution of the sizes of clusters of near-duplicate documents. Bothhelp detect the use of templates in the spam page generation process.
The third is the average change in page content from week to week,which would find sites with content that changes almost completelyeach week (a signal of spamming activity).
3.3 Content Spam Detection Methods A different approach is to use a generative model for documents, such as Latent Dirichlet Allocation (LDA), a paradigm introduced byBlei et al. [30]. In LDA, when writing a document given a languagemodel, the author first picks a topic according to a distribution overtopics, and then picks a word according to a topic-dependent distribu-tion over words. B´ır´ o et al. [29] use a multi-corpus LDA to find, roughly speaking, whether a document is more likely to have been generatedfrom a non-spam model or from a spam model; both models are inferredby training on a set of labeled examples. More recently, B´ır´ improved on that performance by 3–8% by using a linked LDA modelin which topics are propagated along links.
Classifying Pairs of Documents
A number of researchers have focused on the task of detecting nepotisticlinks in a document collection using some or all of the content of thesource and target pages [20, 62, 159, 196]. The assumption in most ofthese approaches is that in a non-spam link, the content of the sourcedocument and the target document should be similar.
ur et al. [20] and Martinez-Romo and Araujo [159] measure the Kullback–Liebler divergence of the unigram language model of bothdocuments and consider a link as nepotistic if it exhibits a very highdivergence. Bencz´ ur et al. noticed that comparing all pairs of docu- ments connected by a link may be computationally prohibitive, so theysuggested comparing only the anchor text (or a few words around it)in the source document with the target document. Martinez-Romo andAraujo additionally explored other subsets of content, including inter-nal links (pointing to pages on the same host) versus external links(pointing to pages on a different host), URL terms, surrounding anchortext, titles, etc.
In the context of comments in a blog, Mishne et al. [167] describe how to detect spam comments in blogs by analyzing the disagreementbetween the language model of a comment and the language model ofthe blog posting to which the comment is directed. This is describedin more detail in Section 7.3.2.
Dealing with Content Spam and Plagiarized Content Detection Using Coding-Style Similarity
A weakness of Web spam that can be exploited by Web spam detectionsystems is that most of it is automatically generated. Urvoy et al.
[225, 224] preprocess Web pages by considering the page as an XHTMLdocument and removing all element names, attribute names and values,and all printable character data. For instance, a Web page such as: <p class="myclass">This is an <b>example</b></p> is transformed into: Next, similarity between the coding style of two Web pages can be computed directly by means of character n-gram comparison, or indi-rectly by using a faster technique such as hash sketches [36] (describedbelow in Section 3.4). If two pages are similar in this regard, they arelikely to have been generated using (variants of) the same template,which can be used as a proxy for authorship. This allows propagationof information about known spammers to other pages sharing the samecoding style.
Malicious Mirroring and Near-Duplicates
Fetterly et al. [75] observed a large number of pages containing text thatwas automatically generated by the "stitching" of random "phrases"copied from other pages. In many cases these were not even phrasesin the linguistic sense, but instead were just word n-grams. To be ableto find phrase duplication on the Web, they resorted to an algorithmictechnique based on sampling that can be used for computing a fastestimation of the size of the intersection of two sets [36].
The phrase-level duplicate detection works as follows. First, each sequence of n-words in a document d (such sequences are referred toas "shingles" [36]) is hashed using a fixed hash function f . This givesnd − n + 1 hashes per document f1(d),f2(d),.,fnd−n+1(d) where ndis the total number of words in the document. Next, a set of m dif-ferent hash functions h1, h2, . . , hm is applied in turn to each of the 3.5 Cloaking and Redirection hashes; we retain the minimum value obtained for each of them, whichis called a fingerprint, hash sketch or simply sketch. Then, a documentis represented by a set of sketches: s1, s2, . . , sm where si(d)  The sketches are useful because they can be used to quickly compute the similarity between two documents. More specifically, let J(A, B)denote the Jaccard coefficient between two sets A and B, definedas J(A, B)  A ∩ B / A ∪ B . Given two documents u and v havingsketches of sizes nu and nv respectively, we have that [36]: where the right-hand side is much faster to compute given that m nu, nv. In practice, Fetterly et al. [75] use m = 84 and compute onsequences of n = 5 words. They found that even after discarding dupli-cates and near-duplicates, about a third of the pages on the Web havemore phrases in common with other pages than phrases that are uniqueto that page. Also, they found that pages with an abnormally highfraction of phrases shared in common with other documents in thecollection are more likely to be spam than to be non-spam.
Wu and Davison [240] also considered the issue of near-duplicates, but focus on duplicate "complete links" in which both anchor text andtarget URL were copied. When a sufficient number of complete linksare found to have been copied, the weights of those links are reducedaccordingly, thus ameliorating much of the effect of link farms andreplicated pages.
Cloaking and Redirection
Cloaking is a technique by which a Web server provides to the crawlerof a search engine a page that is different from the one shown to regularusers. It can be used legitimately to provide a better-suited page forthe index of a search engine, for instance by providing content withoutads, navigational aids, and other user interface elements. It can also beexploited to show users content that is unrelated to the content indexed Dealing with Content Spam and Plagiarized Content by the engine. While they use different mechanisms, redirection andvisual cloaking have a similar effect — the content that a user sees isdifferent from that seen and indexed by the search engine.
Cloaking can be used as a spamming technique to deceive the searchengine, when the page sent to the search engine is semantically differentfrom the page shown to users. Thus, malicious cloaking is sometimesreferred to as semantic cloaking [238, 239].
Detecting if a page is using cloaking is not easy. It may require the search engine to pose as a regular user (e.g., by changing the user-agent header sent to the Web server), which is against broadlyaccepted rules of behavior for Web crawlers [130]. Moreover, searchengine optimization folklore suggests that spammers keep and exchangelists of IP addresses associated with search engine crawlers. Searchengines must vary the IP addresses they use when testing semanticcloaking using a crawler.
It is also not enough to compare two copies (the regular crawler and the browser's-perspective crawler) as there are many dynamic pagesthat can yield false positives. A possible method for detecting crawlingis the following, described by Wu and Davison [239]. First, downloadtwo copies from each page, one from a browser's perspective and onefrom a crawler's perspective. If the two copies are identical, there isno cloaking. If they are sufficiently different, download one more copyfrom each perspective to verify that the difference is not due to normalchanges to the page. If the browser-perspective and crawler-perspectivecopies are equal in the same perspective but different across differentperspectives, flag the page as cloaking.
Once a set of pages employing cloaking has been identified, an automatic classifier can be built to identify which of them are casesof semantic cloaking. Such a classifier is described in [239] and rele-vant features include whether the crawler-perspective has more meta-tags, words or links than the browser-perspective. Recently, Lin [146]proposes methods that leverage HTML tag multisets, particularly for 3.5 Cloaking and Redirection dynamic pages, as the tag structure for a Web page is likely to persistover time even if page contents change.
The above method can be refined by adding intermediate steps to avoid false positives, as suggested by Chellapilla and Chickering [50],and by using external knowledge about the Web. This may includecharacteristics that indicate semantic cloaking, such as the presence ofpopular or highly monetizable queries.
In practice, for a search engine using any of these methods, gener- ating multiple requests for every page in a crawl is simply infeasible.
In such cases, either a sample of suspicious pages, or multiple copies ofthe same file obtained in different visits to the same page for refreshingthe search engine's copies, can be used.
A completely different method for obtaining the browser-perspective is suggested by Najork [173], by using a fingerprint obtained by atoolbar installed in some user's browsers, which is transmitted to thesearch engine and compared with a similar fingerprint obtained fromthe crawlers' perspective.
The 302 Attack
A particular type of cloaking involving HTTP headers was known tothe SEO community as the "302 attack" [206] referring to the HTTPcode for a "temporary redirection".
This attack works as follows. A spammer creates a page U, and submits it to a search engine to be crawled (or includes a link to it inone of the pages the spammer already controls). This page U , whenvisited by a Web crawler, simply returns a 302 code redirecting thecrawler to a reputable page V. Now, in this situation, search engines until circa 2005 would con- sider U and V to be two identical mirrors of the same page, and moreimportantly, would pick arbitrarily one of the two URLs (U or V ) asthe canonical URL — the one to show to the user when showing thatpage as a result to a query. Using this technique, an attacker was ableto lure to his Web site users searching for the content of the reputablepage V , and upon receiving the visit from a normal user instead of acrawler, show spam content instead of a redirection.
Dealing with Content Spam and Plagiarized Content Redirection spam is closely related to semantic cloaking. Instead ofproviding a different copy to the search engine at indexing time, aredirection is performed once a user arrives to a page. This redirectionleads the user to a semantically different page.
This is usually accomplished by using a scripting language such as JavaScript to redirect the user to a spam site. Most search engines donot interpret all the scripts due to the high computational cost of doingso for every page.
Instead of trying to completely interpret the scripts, a search engine's crawlers may try to do some shallow parsing of the scripts totry to reduce the effect of Web spam.1 Unfortunately, there are manycode obscuring techniques that can be used in JavaScript and that hidemalicious redirections [51]. Besides cataloging different code obfusca-tion techniques used by spammers, the authors advocate the use ofredirection detectors based on lightweight JavaScript parsers operatingin a controlled environment (a "sandbox") with an execution timeout.
In practice, the presence of obfuscated JavaScript code is often by itself a strong signal that a page is involved in spam [231]. Given the wayspammers operate, through a few networks that "funnel" traffic to somesites by serving ads that lead to redirections, Wang et al. [231] arguethat detecting those aggregators and funnels that do the redirection forlarge sets of pages is an effective way of eliminating massive amountsof Web spam with less effort than blacklisting individual spam sites.
Finally, spammers may exploit the fact that Web crawlers typically donot render a Web page like a browser does (e.g., with JavaScript,2 fixedresolution screen, etc.) to display contents that are different from theones indexed by search engines.
An old, and generally useless approach today to visual cloaking is to make the spamming text of a page appear in the same color as the 2 In recent years, some search engines have been able to scan within JavaScript and can execute some JavaScript [70].
3.6 E-mail Spam Detection background or so small as to be unreadable. Such an approach, if suc-cessful, would have the effect of making the search engine index all ofthat content but only show the non-hidden content to the user. Simi-larly, one might use CSS to render some of the text in an area that wasbeyond one of the borders of the browser. A more modern approachwould generate content for the user using JavaScript, or perhaps usingan iframe showing the content of a different page to obscure the orig-inal indexed content underneath.3 Many of these attacks can be and are recognized by well-designed crawlers and spam classifiers. When the page's content is more complex,sometimes it is necessary to incorporate more aspects of a regular Webbrowser into the crawler's logic (e.g., [51, 170, 230]).
E-mail Spam Detection
Content-based Web spam detection techniques overlap with the meth-ods used for e-mail spam detection, to the extent that both Web spamand e-mail spam detection can be described as text classification prob-lems. Over the years, the e-mail spam detection community has devel-oped several classification methods. In Section 7.3.2 we will see anexample of an e-mail spam classifier being used to detect commentspam. The reader interested in the state of the art in e-mail spamdetection, can read, e.g., a recent survey by Cormack [55], or start bylooking at the entries in the Spam Track at TREC4 and at researcharticles presented in recent editions of the Conference on E-mail andAnti-Spam.5 Since there are no restrictions on who can publish Web pages, a Webspammer can easily create a Web page and put whatever content isdesired into it. This can include the repetition of terms or phrases tomake the page rank highly for such queries, or arbitrary content so that 3 See for example, Chellapilla's AIRWeb 2006 presentation available at http://airweb.cse.
Dealing with Content Spam and Plagiarized Content the page might be considered relevant for many queries. Content mightbe copied from high-quality pages, or generated artificially, or mightsimply replicate a spam page that has already been copied thousandsof times. Such content might be visible to the user, or hidden in somefashion (by placing the text off of the visible portion of the page, byobscuring it with other content, by cloaking depending on which clientis requesting the page, or by redirecting the user to a new page).
In this section we have discussed such possibilities, and how researchers have worked to identify instances of such Web spam.
Content spam is one of the oldest forms of search engine spam, andimmunity to human tampering via content spam was one of theearly claimed features of Google6 given the introduction of the Page-Rank algorithm. However, once Google became popular, spammersstarted figuring out how to manipulate PageRank and other link-basedmethods, as we discuss in the next section.
6 See for example, the Integrity paragraph at the bottom of a copy of Google's technology Curbing Nepotistic Linking
Citation analysis is one of the key tools used in bibliometrics to assessthe impact of an author, or of a document. The basic assumption isthat citations in texts are not random, but that they indicate thatdocuments are somehow related, and confer authority to the documentbeing cited. Of course, several caveats can be mentioned: citations canbe used to criticize as well as to praise, self-citations are frequent, somedocuments — e.g., methodological papers or surveys — attract a dis-proportionately large number of citations, citation patterns vary acrossdisciplines, many citations on a text are irrelevant, and so on. Lastbut not least, authors who obtain a benefit from having high citationscores have an incentive to try to "game" bibliometrics, for instance,by citing each other frequently in a nepotistic way (independent frommerit), forming a "mutual admiration society" through their citations.
The Web is much more open than traditional publication, and sev- eral forms of citation analysis are used extensively to rank documents.
Hyperlinks can be created on the Web essentially for free, and all thestandard link-based ranking algorithms such as counting in-links [145],computing PageRank [183] or running HITS [122] or SALSA [141] aretrivial or easy to game, unless counter-measures are taken.
Curbing Nepotistic Linking Because of this, three research problems have attracted a consid- erable share of the research effort of the Adversarial IR community:(i) developing methods to detect spamming aimed at link-basedmethods, (ii) determining to what extent that spamming was able tosuccessfully boost ranking, and (iii) studying how to make the link-based ranking methods robust to manipulation. This section deals withthese topics. Other graph-related topics such as trust and distrust areaddressed in Section 5.
This section briefly describes the fundamental link-based ranking meth-ods used on the Web.
PageRank [183] is an estimate of the importance (or equally, authorityor reputation) of a Web page. It is arguably the most successful link-based ranking method, as demonstrated by the Google search engine.
Currently, most search engines probably use some form of PageRank-style computation for ranking, but in practice its contribution to thefinal ordering of pages is believed to be in general small compared toother factors [201]. Nevertheless, PageRank is well known by the searchengine optimization and marketing communities and by spammers.
The computation of PageRank is relatively straightforward.
We start with a graph G = (V, E) representing Web pages V =
{1,2,., V } and hyperlinks E ⊆ V × V . Then, this graph is repre-
sented in a matrix M V × V with
if (i, j) ∈ E Next, a new matrix P is derived from M by adding links with a
small weight from every node to all other nodes in the graph. This
means that P represents a graph that is similar to M, but has the
property of being strongly connected: there is always a directed path
between any pair of nodes. The matrix P is irreducible, and it is
4.1 Link-Based Ranking P = V 1 V × V + (1 − )M,
where 1 is a matrix containing only ones, and  is a small value, typically
0.1 or 0.15 as proposed by the authors of the PageRank paper [183].
Next, the PageRank of a page i is the i-th component of the eigenvector
of P associated with its largest eigenvalue.
PageRank has been studied extensively due to its simple formu- lation. For surveys on many aspects of PageRank, see Langville andMeyer [138, 139]. For a survey specifically on how to compute Page-Rank values efficiently, see Berkhin [23].
The HITS (Hyperlink-Induced Topic Search) algorithm proposed byKleinberg [122] is another method for ranking Web pages. It starts bybuilding a set of pages related to a topic by querying a search engine,and then expands this set by identifying and retrieving incoming andoutgoing links. Next, two scores for each page are computed: a hubscore and an authority score. Intuitively, a page has a high authorityscore if it is pointed to by pages with a high hub score, and a page hasa high hub score if it points to many authoritative resources.
Henzinger [25] is an extension to HITS that attempts to eliminate theeffect of mutually reinforcing relationships in HITS by considering onlyexternal links (removing the links between pages in the same site) andby re-weighting the edges in a manner slightly more complicated thanthat of PageRank. In particular, it adjusts the link weights such thatthe weights of edges of multiple source pages on a single host that pointto a single target page on a different host sum to one when calculatingauthority, and that the weights of links from a single document thatpoint to a set of pages on the same target host to also sum to one whencalculating hubs.
SALSA [141] (Stochastic Approach for Link-Structure Analysis) builds an expanded set as in HITS, re-weights the edges on the basisof the in and out-degrees of the source and target pages, and then Curbing Nepotistic Linking does an alternating random walk on this set of pages, by following inalternating order a link forward and a link backward in the sub-graphsinduced by the selected pages. The ranking induced by this algorithmis equivalent to in- and out-degree when there are no weights and theexpanded set is connected [31].
Link Bombs
A link bomb is the cooperative attempt to place a Web page in theresult list for a (typically obscure) search query. The specific targetengine, Google, leads to the much more common (and original) namefor this technique: Google bombing.1 While there can be different moti-vations, including humor, ego, or ideology [15], the approach exploitsthe use that search engines make of in-links and anchor text for rank-ing. The link bomb organizer will typically attempt to convince manyWeb page authors to use particular anchor text in a link to a particulartarget, so that the target ranks highly for a query corresponding to theanchor text. In many cases, link bombing is considered to be a formof online protest, and is effectively "creating alternate constructions ofreality through collective action online" [220].
This particular type of spam has earned publicity a number of times in the popular press (e.g., [143, 163, 175]). An early publicized attempt,and the source of the term Google bombing was Adam Mathes' 2001Blog post encouraging the creation of a Google bomb for a friend'sblog using the phrase "talentless hack" [162]. While for many years,most Google bombs were created for humor, some, such as the com-petition for the query "jew" [14] were more worrisome. By 2006, thetechnique was being used for political advantage [118]. There havebeen many instances of this type of search engine hacktivism since.2As a result, Google finally addressed the issue with a revision to theirranking system [171].
Finally, link bombing is often the subject of questionable SEO con- tests, such as the one sponsored by the firm Dark Blue in 2004. The 1 The phrase Google bombing has even been included in the second edition of the New Oxford American Dictionary [191].
4.3 Link Farms goal of this contest was to rank highly for the phrase "nigritude ultra-marine" [198], a pair of nonsense word derived from the name of thesponsor. There have been many such contests over the years, oftendemonstrating the lengths to which some SEO practitioners will go.
Link Farms
A link farm3 is a set of pages linked together with the objective ofboosting the search engine ranking of a subset of those pages. Thepages for which the spammer wants to increase the ranking are boostedpages, while the pages used to that end are boosting or hijacked pages,depending on whether they are legitimately or illegitimately under thecontrol of the spammer [91]. The latter can be the case of a publiclywritable Web page (such as a blog page which accepts comments),in which a spammer may post a comment containing an out-link toparticipate in a link farm. (There are specific methods to reduce theeffect of spam in publicly writable pages described in Section 5.2.) From the perspective of those manipulating the search engine rank- ing, we may consider two types of link manipulation that are differentin principle: Sibyl attacks and collusion attacks [52].
In a Sibyl4 attack, there is a single attacker (an individual or a com- pany) that creates multiple identities, in this case multiple Web pagesor sites, not easily identifiable as belonging to the same individual [64].
The purpose of these pages or sites is to boost the ranking of a subsetof those pages belonging to the attacker or to a third party that hiresthe attacker for this purpose.
In a collusion attack, a group of individuals or companies agree to mutually link their Web pages in a manner that is independent of thequality or relevance of each other's resources (e.g., as is the case inmany link exchange sites). This has been termed a mutual admirationsociety [164], and its purpose is to boost the ranking of at least onepage per participant.
The difference is that in the case of the sybil attack, a single page could be boosted even at the detriment of the ranking of all the other 3 Confusingly, a link farm is also sometimes referred to as a "link bomb".
4 Named after the split personality case of Sybil Dorsett.
Curbing Nepotistic Linking pages that have been manipulated. In the case of a collusion attack,each participant must obtain some benefit out of participating. In otherwords, in the sybil attack the benefits can be either concentrated orspread across several sites, while in a collusion attack the benefitscannot be concentrated on a single site (or the other participants wouldnot have incentives to participate). Thus link farms created by collu-sion are a more restricted form of link farms than those created by asybil attack.
In the case where the objective of the spammer is to boost the PageRank of a single page, the best strategy is to have all boostingand hijacked pages link to that target [91]. Depending on the presenceof other constraints, variations of this linking pattern can be employed[5, 66], particularly if the spammer wants to avoid being detected.
If the rest of the Web does not link to any of the attacker's boosting pages, then there is no point in creating a complex structure in termsof increasing the PageRank [54]; in order to substantially change thePageRank of a target, out-links from pages linked to by the rest of theWeb must be created.
If these out-links cannot be obtained, then many new pages have to be created to exploit the "random jump" factor of PageRank. However,this imposes a cost in an attack of a domain-level PageRank, as Webpages can be created easily, but domain names need to be purchased.
Different link farm structures are studied empirically in [12, 247] comparing the PageRank gain of forming a clique or quasi-clique ver-sus other structures such as a star or a ring. The clique yields a muchhigher PageRank gain but it might be much easier to detect than otherstructures; it might also be impractical from the Web site design per-spective if the number of participants is too large.
In the specific case of collusion or semi-collusion [35] (autonomous agents cooperating in some aspects, but competing in others), by intro-ducing some constraints the process of link farm construction can bemodeled formally as a game [106]. One such constraint can be, forinstance, that each page has a limited amount of space to place promi-nent out-links (those with some chance of being clicked by users). In thisgame, the objective of the players is to maximize the expected revenue,that is, per-link revenue times number of visits, for the subset of pages 4.4 Link Farm Detection under their control. For the players, actions in the game correspond toplacement of links in the subset of pages under their control. Severalvariants of this game are further discussed by Immorlica et al. [106].
More generally, the creation of links in the Web or any competitive net-work can be modeled as a game [219]. Using game theory, Hopcroft andSheldon [103] specifically consider how a reputation system (e.g., Page-Rank) can affect the dynamics of link formation and thus the structureof the network. They find that even when the participant models aresimple (and selfish), different reputation measures can lead to dramat-ically different outcomes.
Link Farm Detection
The methods for link farm detection often search for anomalous pat-terns within the interconnection graph of the Web. The huge size of thisgraph reduces the class of feasible methods. A popular class of meth-ods which are considered practical in large-scale applications is that ofsemi-streaming graph algorithms: methods that require O( V ) bits ofmain memory and work by doing a small number, typically O(log( V )),of sequential scans over the edges of the graph [72].
Detecting Dense Sub-graphs
Link farm detection methods are usually aimed at finding dense sub-graphs, a problem for which no efficient exact solutions are known, butfor which there are several approximate algorithms.
One such algorithm is the method for dense sub-graphs described by Gibson et al. [79], which is based on hash sketches [36]. The generalmethod is similar to the one described in Section 3.4 for finding near-duplicate contents; the basic idea is to compute hash sketches oversubsets of elements on a large set, and then use those sketches to quicklyestimate the sizes of set intersections. Instead of using sequences of nwords as we did for contents, in the case of graphs we use sequences ofn links.
The algorithm in [79] works as follows: first, the graph is represented as an adjacency list, and groups of n nodes from each adjacency listare converted into a hash sketch. Next, an inverted index of sketches Curbing Nepotistic Linking is created; this is a list of ordered sketches in which each sketch s isassociated with a list of nodes in the graph, in whose out-links thesequence of nodes represented by s can be found. Finally, to find densesub-graphs the posting lists of each sketch are scanned and groups ofn nodes are sketched again. The sets of nodes associated with valuespresent in frequent second-generation sketches are good candidates formembers of dense sub-graphs.
Another algorithm that can be used for detecting dense sub-graphs is the Approximate Neighborhood Function (ANF) [184], an estimationof the neighborhood size of a set of nodes in a graph, obtained usingprobabilistic counting. An ANF-plot is the plot of the neighborhoodsize of a node for different distances, and a dense sub-graph shouldappear as a growing number of in-neighbors at short distances thatlater "stalls" when reaching the boundary of the link farm [19].
Zhang et al. [247] define the amplification factor of a set of nodes as the ratio between the current sum of their PageRank scores, and thesum of their PageRank scores they would obtain if the links betweentheir members were removed. They show that the amplification factoris bounded by 1/ in which  is the random jump factor, typically0.15; a tighter bound is shown in [12]. Interestingly, the amplificationfactor can be used as a link farm detection strategy: the PageRank iscomputed with different  parameters, and then the nodes that have ahigh PageRank and whose PageRank is strongly anti-correlated with are suspicious of belonging to a link farm.
Wu and Davison [241] discover link farms by first finding a candidate set of pages whose in-links and out-links have a sufficient number ofdomains in common (a kind of dense sub-graph). This list of candidatesis then expanded by finding pages with sufficient links to confirmedcases of spam. In [240], the authors heuristically look for dense sub-graphs (specifically those pages with many examples of anchor textsand targets in common), and consider those found to be examples ofplagiarism.
A completely different algorithmic scheme can be found in Yu et al. [245] which uses random routes. In a random walk, every time therandom walker arrives at a node he chooses at random which out-linkto follow. In a random route, there is a random, but fixed, permutation 4.4 Link Farm Detection applied to the links of all nodes, which uniquely determines the out-going edge given the incoming edge. This also means that a randomroute can be traversed backward without actually storing the route. Inthe SybilGuard method proposed in [245], a node v accepts a node u aslegitimate if, after a few trials, a random route starting at u intersectsa random route from the verifier node v.
Detecting Anomalous Sub-graphs
Another approach is to look for anomalies in general, instead of specif-ically dense sub-graphs. In addition to other signals, Fetterly et al. [74]examined the distribution of in-degrees and out-degrees, finding valueswell beyond the expected Zipfian distribution that corresponded signif-icantly to spam. In Becchetti et al. [18] a number of link-based featuresare extracted from a set of nodes, including degree, average degreeof neighbors, edge reciprocity, etc. Using these features, an automaticclassifier of spam sites is learned using a large set of training exam-ples. Among the features used by these classifiers, a particularly usefulset are neighborhood sizes at short link-distances [19, 184], differentvariants of PageRank [183], and TrustRank [94] which is described inSection 5.3.
Benczur et al. [21] study the distribution of PageRank scores in the neighborhood of a page. Their method includes identification ofsuspicious candidates, candidate set expansion and penalization of thecontributing nodes. Suspicious nodes are identified by looking at reg-ularities in the distribution of the PageRank of the in-neighbors of anode. The more uniform this distribution is, the more likely the linksare placed automatically and are part of a link farm.
Da Costa-Carvalho et al. [59] use several site-level (e.g., host-level) link-based heuristics to detect anomalous linking patterns. Among thepatterns they detect are: mutually reinforcing sites (having high link-reciprocity at the page level), sites that are responsible for a largefraction of the in-links of another site, sites whose in-neighbors have anabnormally high fraction of links between them, and so on.
Caverlee and Liu [46] measure the "credibility" of a page by looking at the quality of its out-neighborhood; they do this by simulating short Curbing Nepotistic Linking random walks starting from the page to be studied. Each page is thenassigned a credibility score reflecting how close and how dominant spampages are in the pages reachable from the current page.
Zhou et al. [253] consider the calculation of "spamicity" in an inter- active browser-based setting. They consider the set of pages that sup-port (have a path to) the page in question, and consider pages whosesupporting page farms are sufficiently effective to be spam.
The methods that perform structural analysis of the link graph can often also be used to find spam in social networks [174] (see Section 7for more).
Detecting Abnormal Link Change Rates
According to Ntoulas et al. [181], the rate of change of links on theWeb is very fast, even faster than the rate of change of contents. Shenet al. [209] study temporal link-based features. These include the rateof growth and death of new in-links and out-links from the perspectiveof entire sites. They show that in practice for spammers these changerates are abnormally high while the link farm is being created, andthis observation can be used to improve the accuracy of a link-basedspam classifier.
The previous section describes methods to detect link farms. This sec-tion describes methods that reduce the effect of link farms withouttrying to determine their boundaries explicitly. This includes findingspecific instances of nepotistic links (Section 4.5.1), or reducing theeffect of nepotistic linking on ranking by considering groups of pagesas a single entity (Section 4.5.2), or by reducing the effect of shortcycles (Section 4.5.3).
Removing or Down-Weighting Links
One might consider the detection of nepotistic links as a form of datacleansing. After detecting suspect link structures, a fairly common 4.5 Beyond Detection approach is to remove anomalous links before estimating authority onthe nodes of the remaining graph [20, 59, 196, 241].
Removing or down-weighting nepotistic links can also be used as a measure that is not as drastic as removing or demoting nodes, andthat can be used in the cases in which the classifier has not enoughconfidence on a spam prediction. In these borderline cases, which aremany, a natural way of dealing with link farms is to down-weight thelinks, enabling the content to still be visible but (hopefully) reducingthe effect of spam (e.g., as in [240]).
Lumping (Merging) Nodes
On the Web, a new page can be created almost for free, which impliesthat the "one-page, one vote" paradigm [145] may not be truly appro-priate. A natural way of making link-based ranking methods robustagainst the creation of multiple pages managed by the same entity isby considering all those pages as a single node in the graph. In Markov-chain theory this is usually referred to as the process of "lumping" somestates in a Markov chain.
As a concrete example, we can use the fact that a domain name has a monetary cost (even if it is only a few dollars per domain) todesign a link-based ranking method that considers all the pages in adomain as a single node. In this way, we effectively ignore the linksamong pages in the same domain, as well as multiple links from onedomain to another.
In the case of PageRank, Caverlee et al. [49] study methods for lumping together nodes in a graph into sets of nodes for reducingspam. These sets can be arbitrary, for instance based on domain names.
If domain names are used to group nodes, a link from a page u in domaindu to a page v in domain dv, is turned into a hyperlink connecting duto dv, and is weighted proportionally to the number of inter-domainlinks starting from du. This effectively reduces the effect of page-levellink farms and generates a domain-level ranking in this case.
Berlt et al. [24] study a different method motivated by the same observation as the one above. They turn the Web graph into a hyper-graph in which links do not connect a node to another node, but a set of Curbing Nepotistic Linking nodes to another node or to another set of nodes. If for instance domainnames are used to group nodes, a link from a page u in a domain du toa page v, is turned into a hyperlink connecting the set of all pages indu to the page v. This counts all the links between pages in a domainto a page in another domain as a single link, and still can provide apage-level ranking.
A related alternative is to split the influence of any given group of pages; in short, having k out-links may mean that each out-link gets1/k of the score mass. Both of these concepts are introduced in theimproved [25] version of HITS. Roberts and Rosenthal [202] furtherimprove HITS by first clustering the set of candidates using a link-based clustering method. Next, only the links between nodes in differentclusters are considered in the computation. If a randomized clusteringmethod is used, this process can be repeated a few times using differentclusterings, and then the scores can be averaged across all the runs.
Reducing the Effect of Short Cycles
Hopcroft and Sheldon [102] propose a link-based method that is basedon the same random walk as PageRank, but ranks nodes according tothe expected hitting time from the restart distribution instead of bytheir probabilities in the stationary state. The authors show that thisis more resistant to manipulation than PageRank given that a nodeu cannot influence its own ranking (e.g., by placing out-links to othernodes that have short paths linking back to u).
Combining Links and Text
Most comprehensive spam detection approaches combine both the anal-ysis of links with content-based analysis from Section 3.
The simplest way to incorporate both content and link information is to provide them both as features directly to the classifier. Simplefeatures of this type might include the number of out-links to the samesite and the number to different sites.
A slightly more involved method examines such features at the neighbors of a node, and provides those features (in aggregated form) tothe classifier. For instance, among other features Drost and Scheffer [65] 4.6 Combining Links and Text computed the feature "number of tokens in title" of a page p. Thismeans they also compute as features for p: the average number of tokensin the titles across all the in-links of p, and the average number of tokensin the titles across all the out-links of p. They used both average andsum as aggregate functions. After a feature selection procedure, theyshow that many of these features, computed from the neighborhood ofa page, rank among the most important ones in the classifier.
Instead of propagating features, one could instead propagate classifications. Castillo et al. [44] include link-based and content-basedfeatures in a classifier to produce a base prediction, then describemultiple methods for post-processing this prediction considering thegraph structure. The first method is to propagate by averaging thebase prediction across the neighbors of a node. A second method isto cluster the graph (for instance, using METIS [120]), and then giveto all the nodes in each cluster the same label (spam or non-spam)using majority voting. A third method, which outperforms the others,is to use stacked graphical learning [131], which is a fast way ofincorporating information from neighboring nodes in a classifier at asmall computational cost.
Gan and Suel [78] explore a related method in which a base classifier is learned, and then the predicted class for a node is refined by doinga weighted majority voting of the predicted classes of the nodes linkedfrom it (but without re-training as in stacked graphical learning). Inthe case of a host graph, the weights can be for instance the number ofpage-level links between two hosts. The weighted majority voting canuse only the in-links or only the out-links of the page. A related methodthat yields a larger improvement is to use a second classifier that usesas features the prediction from the base classifier (but not its featuresas in stacked graphical learning) and statistics about the predictions inthe neighborhood of a page.
Finally, one might optimize the classification of all nodes across a graph simultaneously in which neighbors are expected to have simi-
lar classes. Abernethy et al. introduce WITCH (Web Spam Identifica-
tion Through Content and Hyperlinks) [2, 3, 4]. The classification is
performed using an SVM that includes graph regularization and slack
variables. The SVM receives as input a set of labeled examples (xi, yi)
Curbing Nepotistic Linking for i = 1 . . ; where vector xi contains the feature values for page i,
and yi is a label provided by a human editor. We define this label to
be +1 for spam pages, and 1 for non-spam pages. The goal is to learn
a linear classifier whose prediction is given by f (x) = w · x. In a stan-
dard SVM, vector w contains the parameters of the SVM, which are
learned by minimizing the following function:
R(w · x
i, yi) + λw · w,
where R is a loss function that penalizes the difference between the
prediction and the actual label, for the  examples for which labels are
available. For instance R can be the difference in absolute values, but
other loss functions can be used. The second term is a regularization
, controlled by parameter λ that prevents the coefficients of w from
getting too large, which would produce overfitting.
Graph regularization is included by an extra term that accounts for the graph structure: R(w · x
i, yi) + λw · w + γ
i,j Φ(w · xi, w · xj ),
where γ controls the aggressiveness of the graph regularization, whose
cost is computed over all the links (i, j) ∈ E. The coefficients ai,j are
weights for each link (e.g., the count of page-level links between two
hosts when we are operating in the host graph), and Φ(w · xi, w · xj)
is a cost incurred by the classifier when it predicts a different label for
nodes i and j connected by a link. A natural choice for Φ(·, ·) is
Φ(fi, fj) = (fi − fj)2, which measures the square of the difference between the two predictedlabels. However, a better choice is to use Φ(fi, fj) = max(0, fj − fi)2, which penalizes the case of a non-spam page linking to a spam page, butnot the converse. Actually the best penalization in [4] involves givinga large penalization every time it is predicted that a non-spam page 4.7 Conclusions links to a spam page, and giving a small penalization every time it ispredicted that a spam page links to a non-spam page.
Building a graph-regularized classifier involves many design choices: how much importance to give to the regularization term (parameter γabove), how to describe the cost of predicting different labels for nodesconnected by an edge (function Φ above), how to weight different edges(parameters aij), etc.
To have a significant effect on a link-based ranking method that esti-mates authority, a spammer needs to coordinate many links in the formof a link farm. Most of the anti-spam methods described in this sectionaim at making this process more difficult or ineffective, by re-weightingor removing suspicious links, or by changing the unit of influence fromthe page to host, among other techniques.
In this section we have introduced the fundamental link analysis algorithms. Variations of these approaches are assumed to be utilizedby the major engines to estimate the importance of pages and sites, andthen used as one of many factors for result ranking. As a result, link-based measures continue to be a significant target of Web spam, andmotivate many of the attacks described later in Section 7.
Propagating Trust and Distrust
An aspect of link analysis on electronically mediated communicationsthat have attracted a considerable amount of research is the studyand inference of trust relationships. These methods are related to thebetter-known authority propagation methods discussed in Section 4,but are different because they are given a set of confirmed trustworthyand untrustworthy agents as inputs.
Trust propagation methods employ the labeled agents in a way that tends to match the heuristics that we apply in our social lives. Forinstance, in the case of untrustworthy agents, we try to apply the "guilt-by-association" heuristic; while in the case of trustworthy agents, wetry to apply the "a friend of a friend is my friend" heuristic.
On the Web, the input sets can be thousands of hand-labeled pages or sites, and the propagation can occur through forward or reversehyperlinks. Trust-aware methods such as the ones discussed on thissection have been shown to be successful at countering ranking manip-ulation on the Web.
Trust as a Directed Graph
The concept of a "Web of Trust" was first introduced in large-scalesystems during the design of key-management protocols for PGP 5.1 Trust as a Directed Graph (Pretty Good Privacy) [256]. A Web of Trust is a directed graph wherenodes are entities, and arcs indicate a trust (or distrust) relationshipbetween two entities.
The Web of Trust in a large community tends to be very sparse. Any given agent interacts only with a small fraction of the members of thecommunity, and thus can only assess the trustworthiness of a handfulof other agents. A natural way of alleviating this sparsity problem is toaggregate the ratings given by several people, usually through the useof some sort of propagation mechanism.
According to the taxonomy presented by Ziegler and Lausen [255], there are two basic types of trust computation: local and global. In alocal trust computation, trust inferences are done from the perspectiveof a single node, and thus each node in the network can have multipletrust values. In a global trust computation, trust inferences are com-puted from the perspective of the whole network, and thus each nodehas a single trust value. In both local and global trust scenarios, thecomputation can be either centralized or distributed among a numberof peers.
In the specific case of trust for Web search, under current technolo- gies the most relevant case is global trust propagation computed in acentralized manner. Guha et al. [89] study global methods for propaga-tion of trust and distrust in a systematic manner. Let G = (U, T ) repre-sent the explicit trust ratings, with a set of users U , and let T representa trust relationship, where T ⊆ U × U with (u, v) ∈ T ⇐⇒ u trusts v.
Let S ⊆ U × U represent the implicit trust between users that isinferred by the system, so that (u, v) ∈ S implies that given the avail-able evidence, u should trust v.
To build the relationship S, we start obviously by considering (u, v) ∈ T ⇒ (u, v) ∈ S. Next, Guha et al. note four different propa-gation types: Direct (transitive) propagation: (u,v) ∈ T ∧ (v,w) ∈ T ⇒ (u, w) ∈ S Co-citation: (u,v) ∈ T ∧ (u,w) ∈ T ∧ (s,v) ∈ T ⇒ (s,w) ∈ S Transpose propagation: (u,v) ∈ T ∧ (w,v) ∈ T ⇒ (w,u) ∈ S Trust coupling: (u,v) ∈ T ∧ (w,v) ∈ T ∧ (s,u) ∈ T ⇒ (s,w) ∈ S.
Propagating Trust and Distrust In most of the research we describe next, trust propagation is notbinary, but is real-valued. Direct (transitive) propagation occurs, butmost approaches will degrade it by some amount. Although Guha et al.
found that all propagation types were useful in a combined trust prop-agation system, most of the work described here focus on the use ofdirect propagation on the Web graph, the reverse Web graph, or both.
Positive and Negative Trust
In many communities the base assessments from which trust is com-puted include both positive (u trusts v) and negative (u distrusts v)assessments. However, most of the research focuses on the propagationof trust, and much less on how to deal with distrust. The reasons arethreefold.
First, the semantics of trust propagation ("the friend of a friend is my friend") are clear and effective in practice, while the semanticsof distrust propagation ("the enemy of my enemy is my friend") havebeen shown to be less effective in practice. For instance, according to theresults of Guha et al. [89], a good method for global trust computationuses an iterative (multi-step) direct propagation of trust, but only asingle-step direct propagation of distrust.
Second, in many communities positive assessments are dominant, as people are much more cautious when providing negative judgments forfear of retaliatory negative feedback, or simply to avoid further unpleas-ant interactions [200]. This means that in some cases the absence of atrust rating after an interaction cannot be considered automatically asa neutral rating.
Third, in the case of the Web in particular, there are no labels on the edges that allow the separation of hyperlinks indicating trustfrom those that might not reflect trust. To alleviate this, there are twoproposals to annotate hyperlinks, both involving adding an attributeto the hyperlink XHTML tag <a>. VoteLinks [157] suggests toannotate hyperlinks with rev="vote-for", rev="vote-against", and rev="vote-abstain" to indicate respectively positive, negative, andneutral opinions. The nofollow proposal [156] which presently is moreused in practice suggests that hyperlinks indicate trust except when 5.3 Propagating Trust: TrustRank and Variants they are annotated with rel="nofollow" where they should be con-sidered neutral in the sense of conferring trust. See Section 7 for morediscussion of nofollow.
Given that the nofollow tag is used but not widespread, learning to recognize the polarity (positive, negative, or neutral) of a link is key.
In experiments by Massa and Hayes [161], over the Epinion community,in which each opinion can be seen as a link, they observed a substan-tial disagreement between the scores obtained by computing PageRankconsidering both positive and negative links, compared to consideringonly the positive links.
Propagating Trust: TrustRank and Variants
TrustRank is a well-known trust propagation mechanism for Web pagesproposed by Gy¨ ongyi et al. [94]. The TrustRank method uses a small seed set of non-spam (trustworthy) pages that are carefully selectedby human editors. Next, a random walk with restart to the seed setis executed for a small, fixed number of iterations. In [94], the restartprobability is the same (0.15) as in the original PageRank paper [183],and the number of iterations is 20. TrustRank has been shown to bevery effective in demoting spam pages in the original paper as well asin later studies.
A closely related concept is the relative spam mass [96] of a node. It is defined as the fraction of its PageRank contributed by spam nodes.
Given that assuming a priori knowledge of which are the spam nodesis unrealistic, the relative spam mass of nodes has to be estimated.
A method for estimating the relative spam mass of nodes is to computea (good)-core-based PageRank, which is basically a TrustRank scorecomputed over an order of magnitude larger seed set. The seed set forthe spam mass estimation should include not only the highest qualitynodes, but many diverse non-spam nodes. The relative spam mass isestimated as the PageRank of a node minus its score as obtained usingthis procedure.
Several variants of the original TrustRank can further improve its efficiency. TrustRank scores tend to be biased toward large communitiesrepresenting popular topics on the Web. Topical TrustRank [243] tries Propagating Trust and Distrust to alleviate this problem by computing several independent topic-dependent TrustRanks for each page, starting with a topic-specific seedset in each run. This is inspired by the way in which Haveliwala [97]computes topic-dependent PageRanks.
Another improvement is to step out of the random-walk paradigm and look at the TrustRank computation as an iterative scoring func-tion and not as a Markov process. In this sense, Wu et al. [242] andNie et al. [178] propose alternative ways of "splitting" the trust massof a node among its out-neighbors, and of aggregating the trust massreceived by the in-neighbors of a node. While the original formulationsof TrustRank and PageRank divide this score by the out-degree of anode, there are alternatives. For instance, the score can be divided bythe logarithm of the out-degree, or not divided at all. For the aggrega-tion of the trust mass received, the nodes can use a summation, as inthe original formulation, or take the maximum trust received from anin-neighbor, or take the sum capped to be at most the maximum trustreceived from an in-neighbor. Their results show that these alternativesimprove over the original formulation in terms of demoting spam.
Finally, seed selection is another important aspect to take into con- sideration when using TrustRank. Under certain conditions, an auto-matically selected large seed set (which may contain a few errors) ispreferable to a manually selected cleaner, but smaller, seed set [110].
Zhao et al. [250] go further, detailing a semi-automatic mechanism tofind both good and bad seeds for use in detecting spam.
Propagating Distrust: BadRank and Variants
The opposite of TrustRank is known in the SEO community as"BadRank" [214]. The intuition behind it is that while the in-linksof a page are not under the control of its author, the out-links of apage can be edited freely by the author and thus creating a link toa spam page means participating in the spamming activity. There isstrong evidence that indeed, non-spammers do not link to spammers ingeneral [44]. BadRank, also known as "anti-TrustRank" can be imple-mented as a random walk that follows links backward, and restarts toa known set of spam nodes; the "badness" of a page is its probability 5.4 Propagating Distrust: BadRank and Variants in the stationary state of this random walk. It has been shown experi-mentally to be effective in detecting spam pages in [135].
In general, once a group of Web pages or hosts has been confirmed to be spam, it makes sense to attempt to automatically find the otherpages, hosts, or domains participating in the spamming activity. Also,in practice in a large search engine a group of assessors (editors) mayhelp in the labeling of spam sites in a semi-automatic setting. For this,a system must present a set of candidates to a human operator. In bothcases, we need ways of expanding a set of confirmed spammers into aset that is very likely to be spam (that we can automatically label tobe spam with high confidence) and a set of very suspicious sites (thatcan be presented to a group of editors for confirmation).
For link farms, given a suspicious node, the nodes contributing a large share of their PageRanks can be detected using a greedymethod [252], and the properties of this group can be analyzed to clas-sify it as a link farm or not. Similarly, Andersen et al. [8] present anefficient approximate algorithm for computing the δ-contributing set ofa node v, which is defined as the set of nodes that contribute at leasta δ fraction of v's PageRank. Their algorithm examines a small subsetof nodes, O(1).
Another automatic expansion method is to use SimRank for spam detection as suggested by Bencz´ ur et al. [1]. SimRank is a generalization of the co-citation and can be used as a feature for a spam classifier, asa page that has a high link similarity (as measured by SimRank) to aspam page is likely to also be a spam page.
Random walks can also be used to expand a set of known spam pages. Wu and Chellapilla [237] start with a given set of confirmedspam nodes and then walk randomly to find other nodes that might beinvolved in the same spam activity.
Metaxas and DeStefano [164] suggest a graph-based method in which the in-links of a set of confirmed spammers are followed recur-sively for a few levels, and then all the nodes in the strongly con-nected component containing the confirmed spammers are labeled asspam. Other expansion procedures, including a triangle-walk methodthat expands a suspicious set of attackers while they form triangles,are described in [210]. Similarly, Wu and Davison [241] perform a Propagating Trust and Distrust discretized propagation — that is, new pages are only added to theset of spamming pages if there is sufficient evidence, but once added,may push other pages above the threshold.
Considering In-Links as well as Out-Links
A number of researchers have proposed methods in which both in-links and out-links are taken into account, propagating both trust anddistrust [178, 242, 248]. Zhang et al. [248] describe two interrelatedpropagation process, which depend on each other, and that propagatescores through in-links and out-links.
Specifically, two values are computed for each page p: one value Qc(p) depends on in-links, and is computed iteratively using the fol-lowing update rule: c(p) = + (1 − α) where o(p) is the number of outlinks of page p. The other value Q(p)depends on the out-links, and is also computed iteratively as: (p) = + (1 − α) where i(p) is the number of in-links of page p. The values Qc(p) andQ(p) are initialized using a list of known spammers, which are assigneda value close to 1, and a list of known non-spammers, which areassigned a value close to +1.
Considering Authorship as well as Contents
A subtle but interesting aspect of trust on the Web is that most mod-els deal with assessing the trustworthiness of an entity that producescontent (the author of a Web site for instance), while the actual goal isto determine how trustworthy a piece of content is [80]. For instance,even in the absence of trust assessments, a piece of information that isrepeated by several independent sources can be considered trustworthy;by the same reasoning, a piece of information posted by a reputable 5.7 Propagating Trust in Other Settings source may be considered untrustworthy if it is contradicted by a largegroup of independent sources of lower trustworthiness.
This type of concern is particularly relevant when examining docu- ments having multiple authors. For instance, Wikipedia articles areauthored by many volunteer editors and a reader might be inter-ested in knowing how trustworthy a particular passage of an article is.
Mc Guinness et al. [90] annotate sentences using the reputation of theirauthors as source information; the reputation of authors is obtained bylooking at the citations of the articles in which they participate. Adlerand de Alfaro [6] and Hu et al. [104] also look at the trustworthi-ness of passages of Wikipedia considering that a passage written by auser u that remains unchanged after an edit of another user v, might beconsidered as "approved" by user v and thus can be considered moretrustworthy.
With rare exceptions [7], authorship of general Web pages cannot be established easily at this time, but if widely accepted mechanismsfor indicating authorship develop over the years, the issue of comput-ing content-level trust from entity-level indicators will become morerelevant in practice.
Propagating Trust in Other Settings
There are other environments in which trust propagation has been stud-ied; they include online social networks, e-mail networks, and peer-to-peer (P2P) networks. Several methods of trust propagation in onlinesocial networks are described in Section 7.4.1. Methods for trust prop-agation in e-mail and P2P networks are related but not central to thetopic of this survey, so we provide only a few pointers in this section.
Trust propagation in e-mail networks is studied, among other authors, by Boykin and Roychowdhury [32] where the sub-graphinduced by legitimate and spam e-mail messages are shown to be clearlydifferent. A related study is due to Gomes et al. [83].
Trust propagation in a P2P network requires decentralized trust computations to establish the quality of the files offered by each peerfor download. A taxonomy of P2P reputation systems is introduced byMarti and Garcia-Molina [158]; this taxonomy considers factors such as Propagating Trust and Distrust how the information is gathered and aggregated and what the actionstaken by the system are with respect to inauthentic peers. A well-knownexample of a mechanism for trust computation is EigenTrust [119]which is shown in simulations to reduce the ratio of inauthentic down-loads in P2P networks with malicious peers. Other approaches includePeerTrust [244], Credence [228], and JXP [186].
As discussed in Section 2.4.3, often detecting spam or providing anestimate of trust is only a means to an end. The output of these methodsneeds to be utilized to achieve a more complex goal, such as improvingranked retrieval in Web search.
Nie et al. [176, 177] show one way of utilizing trust estimates in retrieval systems. After a page-level trust metric has been com-puted, they integrate it into calculations of authority (e.g., PageRankor SALSA). They use the trust value to affect the probability of fol-lowing outgoing links (e.g., emphasizing the "votes" of trusted nodes)and to affect which next node to select (i.e., a non-uniform selectionof out-links, or for random jumps in PageRank). The authors demon-strate improved retrieval performance with their approach when uti-lizing trust estimates computed from multiple algorithms, includingTrustRank.
To some extent, truly authoritative pages are also trustworthy.
However, as we have seen in Section 4, estimates of authority can besignificantly affected by nepotistic links. Thus, in this section we haveseen that it is beneficial to explicitly consider trustworthiness, and howgraph locality permits estimates of trust to be calculated based on thetrustworthiness of known peers. Such trust information can be used toidentify or to demote spam pages, or integrated into ranking algorithms.
Detecting Spam in Usage Data
Usage information has attracted considerable attention in the researchcommunity in recent years, and it is one of the current frontiers inWeb search. Data from search logs, browsing logs, or ad-click logsobtained from different sources are used extensively by modern searchengines that use the "wisdom of the crowds" contained in them to rankdocuments.
As we mentioned in the introduction, the best strategy for spam- mers is to game any signal they believe is used for search engine spam.
By issuing automated queries and clicks, spammers try to fool searchengines into believing that certain documents are more relevant thanothers. By issuing automated clicks, spammers try to inflate the num-ber of clicks received by a given ad to defraud those who advertise onthe Web. Both types of manipulation are studied in Section 6.2.
Fortunately, usage analysis can also be employed against spam- mers, as machine-generated data tend to be statistically different fromhuman-generated data. Section 6.3 outlines how to use signals fromusage analysis against spammers as a way of improving Web spamdetection systems.
Detecting Spam in Usage Data Usage Analysis for Ranking
Ranking Web pages is a difficult problem, and large-scale search enginesare able to produce relevant results by considering a combination ofmany different factors [201]. The activities of users are an importantsource of information that has been started to be used extensively overthe last few years.
Usage information consists of triples u, t, e where u is a possibly anonymized user identifier (e.g., a unique user-id, a unique browsercookie, or an IP address), t is a timestamp, and e is an event. Usagedata sets for large populations or extended periods of time are huge,and can be very noisy and sparse (e.g., for a particular page or query wemay have very little information). From the perspective of commercialsearch engines, usage information is found in three main forms: (1) Search logs (query logs) which include the keywords searched by the users and the pages on which the users clicked.
Sequences of actions are usually referred to as query sessions.
(2) Browse logs obtained from users that opt-in to a system for tracking their activities, e.g., by specifying this in their pref-erences when installing toolbar software. Sequences of actionsare usually referred to as browsing trails.
(3) Ad-click logs in the case of search engines that also operate, or have agreements with, ad networks of pay-per-click ads.
Query logs can be used as a source of information for search engine ranking by boosting the pages that are more clicked by users for a givenquery, being careful to account for the positional bias [114, 57]. Eye-tracking studies and query click-through logs have shown that usersstrongly favor search results shown near the top of the search engineresults page. The fact that certain areas of Web pages tend to be clickedmore often independently of their relevance to the user task has beenobserved for several types of Web pages (not only search result pages),and it affects the interpretation of Web clicks in general [115].
Browse logs can also be used to improve search engine rankings; for instance BrowseRank [151] builds a continuous-time Markov chainfrom browsing trails, and then considers that the pages with the highest 6.2 Spamming Usage Signals probability in the stationary distribution should be ranked higher,much as in PageRank but considering users' browsing activities as tran-sitions and not hyperlinks.
Ad-click logs are used extensively to improve the click-through rate of ads shown to the users, as these clicks represent a large share of theincome of search engines. In the case of ads that are displayed alongsearch engine results for a query (known as sponsored search), the meth-ods are based on estimating the expected revenue of a click, which isa function of the advertiser bids and the expected click-through-rate(CTR). This estimate has to be computed for a particular ad, in a par-ticular slot (position), for a particular user, issuing a particular query.
Given that often previous information about a specific combination ofuser, query, ad, and slot may be scarce or simply not available, theCTR is estimated by a prediction that aggregates information about,e.g., similar ads, close-by slots, similar queries, or similar users. Thismeans that a malicious user issuing queries and then clicking (or notclicking) in an untruthful way may affect the CTR estimations for otherusers and thus manipulate indirectly the frequency with which certainads are shown. For instance, a spammer operating on behalf of a com-pany may issue many searches for the name of a product and thenskip the ad of a particular competitor, clicking in other parts of theresults page, in an attempt to reduce the chances of that ad beingshown.
Spamming Usage Signals
In this section, we discuss three notable types of spamming behavior inwhich spammers attempt to corrupt the usage information that a searchengine uses. Click fraud (Section 6.2.1) interferes with the analysis ofclicks that would otherwise be the result of an unbiased human brows-ing the Web. Search spam (Section 6.2.2) interferes with the analysisof queries received by a search engine as they now include automatedand intentional queries that do not reflect real human usage. Referrerspam (Section 6.2.3) interferes with the analysis of Web site visitationlogs as they may include recorded sources of visits that in fact do notcontain links to the Web site in question.
Detecting Spam in Usage Data Click Fraud
Click fraud is the practice of skewing pay-per-click advertising data bygenerating illegitimate events [187], an activity that is prevalent andpotentially very harmful for the sponsored search business model [109].
Many cases of click fraud are as follows: a content publisher has an agreement with an advertising network that will select ads to placein the pages of the publisher; then, the advertising network will paythe publisher for each click on the ads. Under these conditions, thepublisher has an incentive to generate as many clicks as possible onthe ads. These clicks can be generated by automated scripts, or byhiring people in low-income countries to browse the Web and click onads [226]. In both cases, the spammer needs to conceal the fact thatthe clicks are automatic or all originating in the same geographicalarea, for instance by hiding behind a proxy or several layers of proxies(a technique known as "onion routing"). Another option is the use oflarge botnets, which, through their size, can conceal the behavior byspreading it out over thousands of infected machines [61].
Other cases of click fraud are more subtle: if two companies advertise similar products on the Web, there is an incentive for one company toclick on the other's ads and thus deplete some of the advertising fundsof its competitor, in some reported cases up to 30–40% of them [153].
In general, there are several possible responses to click fraud [109].
One part of the solution is monitoring (either by the search engineor by a third party) and filtering the click streams to discard fraudu-lent clicks. Another component of the solution may be to move towardpay-per-action, in which the payment is made not whenever a clickoccurs but when the user actually buys the product or service beingadvertised. However, there are barriers to the widespread adoption ofpay-per-action, including the fact that it might require companies toshare potentially sensitive data about business transactions with theadvertising networks.
Estimates of expected revenues from clicks (based on click- through estimations) are in general susceptible to spamming activities.
Immorlica et al. [107] describe estimation methods based on observ-ing the previous impressions and clicks for an ad. They show that for 6.2 Spamming Usage Signals a broad class of methods, a spammer that manipulates some of theimpressions and clicks on them, can increase the average payment ofan advertiser.
Metwally et al. [165] study how to detect fraudulent click coali- tions. The underlying hypothesis is that an automatic clicking systemis composed of several agents (automated programs, or humans hiredfor this task) that attempt to generate deceptive clicks for more thanone "customer", for efficiency reasons. Thus, a detection method canbe based on recognizing groups of pages or ads that share a number ofvisitors much larger than what would be expected by chance.
Search Spam
In the case of sponsored search (ads shown along search engine results),a malicious user may affect CTR estimations for ads both by search-ing and then clicking in some ads, as well as by searching and thennot clicking in some ads. This is one of the reasons why not only auto-mated clicks, but also automated searches, should be detected by searchengines and labeled as such in a query log. Other reasons might includeviolations of the terms-of-service of a search engine by attempting todownload and copy a subset of the retrieved result pages. On top ofthat, often these result pages are used as base content to be mixed withspam content, during the creation of content-based spam pages usingthe methods described in Section 3.2.
In an analysis of a 15 million entry search engine query log from 2006, Zhang and Moffat [249] recognized that the log included queriesthat originated from external sources such as Web APIs, toolbars, andother third-party programs. When they examined the hundred largestsessions, they found that about 90% of them were machine-driven,though they did not attempt to figure out if they were generated erro-neously, intentionally, or maliciously.
Buehrer et al. [38] studied automated search traffic on a large-scale Web search engine. They found that given a sequence of queries labeledwith the client IP address and associated with HTTP cookies, it waspossible to classify the sessions into normal and automatic traffic withover 90% accuracy. Salient features in this classifier include the number Detecting Spam in Usage Data of queries, the entropy of the keywords in the queries, the number ofqueries issued in a short (ten seconds or less) period of time, and theclick-through rate. Duskin and Feitelson [67] also consider this issue,and suggest that the interaction between query submittal rate andminimum inter-query dwell time would be a useful feature.
A persistent, but relatively low-impact spamming technique is toemploy a Web crawler that selectively retrieves Web pages but insteadof including a referrer field in the HTTP request to show the source ofthe link being followed (as a browser would) or leaving the referrer fieldempty (as other Web crawlers do), the spamming Web crawler wouldinclude a target URL in the referrer field. The goal, presumably, is toattract links to the target URL from automatically generated (and pub-lished) pages containing Web logs excerpts (or perhaps human trafficfrom attentive webmasters who might be curious why their page isgetting traffic from unexpected places1).
While referrer spam has not been extensively investigated in the scientific literature (an exception is Yusuke et al. [246]), it has been sig-nificant enough to merit its own entry in Wikipedia2 and has a categoryin the Open Directory Project.3 Fortunately, the crawlers that producethe referrer spam can often be detected as non-human [185, 215, 218],and thus their activities, in theory, can be filtered from usage logs.
Usage Analysis to Detect Spam
The previous section showed how spammers can interfere with usagesignals. In this section, we briefly describe how usage signals can beused to improve Web spam detection systems. The usage data thatcan be exploited by search engines are browsing logs (e.g., captured 1 For instance, and were both listed as referrer more than a dozen times in the span of two months for refer-ences to pages on the host.
2 spam.
6.3 Usage Analysis to Detect Spam using a toolbar), which are discussed in Section 6.3.1; and search logs,discussed in Section 6.3.2.
Information from browsing logs can be used to help detect Web spampages.
Bacarella et al. [11] analyze the traffic graph, a graphical representa- tion of the browsing trails of users, in which nodes can be subsets fromthe Web, for instance pages or Web sites, and edges between two nodesu, v, contain information about users visiting u and v in sequence. Aninteresting measure in the traffic graph is the relative traffic, definedfor a site v as the average fraction of the traffic of the in-neighbors ofa v in the traffic graph, which is converted into traffic for the site v.
For instance, if site v is the next site visited by 50% of the visitorsof u and 70% of the visitors of w, then its relative traffic is 60%. Siteshaving very high relative traffic (over 90%) were empirically found tobe mostly spammers, attracting visitors by deceptive means includingpop-ups, pop-unders, redirects, etc.
Liu et al. [150] study both a query log and a browsing log to discover anomalies in some Web sites. They showed that Web pages that do notattract in-links or visits from in-links, but whose traffic relies almostcompletely on search engine-originated visits, are much more likely tobe spam than non-spam. Other features that they show to be useful fordetecting spam pages are the probability of clicking an out-link afterarriving to a page (low probability for spam pages, meaning the click-through rates of spam pages are low), and the number of pages on a siteviewed by users visiting the site (low number of pages for spam sites).
Query Search Logs
The query logs of search engines contain valuable information aboutpopular queries, which are an attractive target for spammers.
Ntoulas et al. [182] and Castillo et al. [44] use this observation to create features for content-based Web spam detection. For instance,a list of the top popular queries submitted to a search engine can be Detecting Spam in Usage Data assembled, and then a page can be considered suspicious if it containsan abnormally high fraction of those queries.
The ad-click logs of sponsored search can also be used to build lists of monetizable queries, those that attract high bids or many clicks fromusers. Monetizable queries are the queries that generate more revenuefor the search engine. Chellapilla and Chickering [50] show that bothpopular queries and highly monetizable queries, particularly the latter,attract much more cloaking spam that other queries.
Finally, when automatically generating content for creating content spam (any of the types of mentioned in Section 3.2), spammers maygenerate pages that are shown in search engine results for many unre-lated query terms. If some relationship among terms can be inferredfrom a query log (e.g., by looking at queries that generate clicks on thesame documents, or a more general co-click relationship of this type),then a feature for Web spam detection can be built. This is studiedin [41, 42] where Web pages that attract traffic for many unrelatedqueries are considered more likely to be spam than other pages.
In this section we recognize that activities on the Web are recorded,and that these records can be automatically aggregated to generate auseful signal, e.g., for ranking of both editorial results and advertising,as well as for charging advertisers. We have seen that adversaries maywant to manipulate "the system", not only to influence the rankingof their pages, but also to affect the advertising payments made by acompetitor or the payments to a publisher site. In the following sectionwe consider how spammers are not only involved in records of what wehave done, but in the content that we generate online.
Fighting Spam in User-Generated Content
User-generated content in so-called social media, as opposed to profes-sionally generated content from traditional media, has been a strongforce behind the growth of the Web since the early 2000s, and a keyaspect of its unique character as a communications medium. Over time,more and more users participate in content creation, rather than justconsumption. Using widely available digital tools, people are becomingproducers and consumers: "prosumers", a term coined by Alvin Tofflerin 1980 [222]. Approximately one-third of users contribute content tothe Web, as measured by studies performed in the U.S.A. [77] andChina [144].
Popular user-generated content domains include blogs and Web forums, social bookmarking sites, photo and video sharing commu-nities, as well as social networking platforms such as Facebook andMySpace. These opportunities are embraced by the majority in a con-structive way, but abused by a minority that disrupts these platforms,or use them for deceptive or fraudulent purposes. Sections 7.2 and 7.3describe how platforms for user-generated content are exploited byspammers to manipulate search rankings. Section 7.4 describes disrup-tive and deceptive activity in the social media platforms themselves.
Fighting Spam in User-Generated Content User-Generated Content Platforms
There are basically three kinds of platforms for user-generated contentthat are attacked by spammers.
Free hosting sites. Most blogs are hosted on sites that offer free
blog hosting and creation tools, usually in exchange for adsin the blog pages. Spammers use these hosting sites to cre-ate splogs ("spam blogs"), fake sites that present themselves asuser-generated content while being machine-generated for thepurpose of spamming.
Publicly-writable pages. Part of the user-generated content is gath-
ered and aggregated through open systems in which anyone canwrite. These include opinion forums and comment forms, userreview sites, and collaborative editing tools known as wikis.
Social media sites. User-generated content is also shared through
sites in which users can upload content (images, videos,answers, etc.), and interact with the content through annota-tions, tags, votes, etc. Furthermore these sites usually allowpeople to interact with each other through social networkingfeatures.
As noted by Heymann et al. [101], apart from approaches based on detection of the spam items or the demotion of them in search results, apreventive approach is possible in the context of user-generated content.
Social media sites can use CAPTCHAs or similar mechanisms to slowdown automatic registration and automatic posting of content, or limitthe number of users that can be affected by a single action. For instance,social media sites impose limits in contact lists or in the number of usersthat can receive any single message. Additionally, in social media sitesusers can, and usually do, help with policing bad behavior by reportingabuse and spam.
However, preventive approaches have disadvantages that have to be balanced with the obtained benefits. In the case of comments, methodswhich require user effort such as registration may reduce the numberof spontaneous responses [167] that may be valuable for the authorsseeking comments from their readers. Something similar happens in 7.2 Splogs the case of Wikipedia, where spontaneous additions and corrections bycasual readers are encouraged and must be balanced carefully againstthe prospects of vandalism.
Zittrain [257] are by their very nature susceptible to abuse. But asa general design principle the counter-measures to keep abuse undercontrol should be postponed as much as possible, to give time for thesystem to mature and for its actors to develop social norms for deal-ing with abuse and spam. This is particularly important in large-scalesystems in which borderline cases are frequent.
Spam blogs are a particular and prevalent type of spam page. Somesplogs are simply spam sites hosted in free hosting sites, and as usualtheir main aim is to deceive the algorithms of search engines to boostthe ranking of some set of pages. Other splogs sites also try to deceiveusers either to click on ads, or by (falsely) presenting themselves asindependent opinion sources about a product or service.
Another communication channel that is abused by spammers are blog pings. "Pings" are a lightweight mechanism by means of whichblog platforms signal that new content is available to blog indexing oraggregator sites. The system that receives a ping adds the blog thatsent the ping to a crawling queue. Splogs tend to generate an abnor-mally high number of pings; in that context, spam pings are sometimesreferred to as spings.
Kolari et al. [127] present a characterization of the splogosphere based on a blog collection and on a collection of pings. The blog collec-tion was provided by BlogPulse1 and corresponds to 1.3 million blogsduring a 21-day period in July 2005. The ping collection was providedby Weblogs.com2 and corresponds to 15 million pings during a 20-dayperiod in November 2006. With respect to classification of splogs, theyshow that it is possible to build an automatic classifier using content-and link-based features as for other spam pages [128].
Fighting Spam in User-Generated Content In contrast to authentic blogs, spam blogs generate more pings (75% of the pings in [127]). Also, the periodicity of pings is abnormalin the case of spam blogs. Legitimate users are more likely to post blogentries during the day than late at night. This is actually observed inthe data if blogs that are likely to be concentrated in a single time zoneare selected; in the case of [127], the authors examine blogs written inItalian and observe a clearly periodic behavior, with daily periodicityand a peak frequency more than 10 times larger than the valley fre-quency. In the case of blogs written in English, given that these blogsare distributed across several time zones, the periodicity of pings is notas sharp as in the case of Italian, with peak frequency (at U.S. workinghours) about twice as large as valley frequency.
The classification of a splog, however, typically occurs after crawl- ing and indexing of the blog has taken place. Given the high ratio ofpings coming from splogs, a lighter-weight alternative is the detectionof splogs through their pings. Kolari et al. [126] propose a meta-pingserver that would receive notifications from ping servers but also utilizereader feedback, blacklists, whitelists in order to provide a filtered pingservice which can then be fed to an indexing system.
Lin et al. [147, 148] look specifically at temporal patterns in blog postings in order to separate authentic blog from spam blogs. Threemain observations are derived from their study. First, normal bloggerspost at a regular but not precise time, while splogs show machine-likeregularity (e.g., posting a new item exactly every three hours). Second,in splogs the distribution of content into topics varies either very rapidlyor not at all, signaling either content plagiarized at random from theWeb or a single source of content that is reproduced over and over.
Third, splogs exhibit a smaller variability in their links over time thanauthentic blogs. These observations can be used to build features thatcapture regularity and self-similarity of temporal patterns, and thesefeatures can yield substantial improvements in the accuracy of a splogdetection system.
Sato et al. [205] classify keywords appearing in Japanese splogs according to two dimensions. One is the informational content of thekeywords, basically its inverse document frequency as described inSection 3.1.2. The other dimension is whether the keyword is long-lived 7.3 Publicly-Writable Pages or short-lived (e.g., a burst). The analysis of the keywords may lead toinsights that help build better splog detection systems. Also, they foundthat a few professional spammers were responsible for the majority ofthe splogs in Japan.
Getting feedback and collecting ideas and opinions from the users isimportant for Web site authors and developers, but comment and opin-ion spam are annoyances for both Web site authors as well as for theusers.
Forum Spam
Discussion boards and forums are among the oldest kinds of user-generated content with roots in bulletin board systems in the decadesbefore the Web. Today, however, such wide-open sites are a visibletarget to spammers, and as a result forum spam is widespread, andis typically used to increase link-based authority [179]. All kinds ofpopular forums suffer from such spam.
Fortunately, many of the methods to detect or ameliorate comment spam, discussed next, can be applied to forum spam.
Comment Spam
Currently, two large commercial providers of comment spam protection(Akismet3 and Mollom4) indicate that they receive more spam com-ments than legitimate comments in the Web site of their customers.
As of April 2010, Mollom reports 90% of the messages they process arespam, while Akismet reports that 83% of the comments they processare spam. The services have different user bases and protect somewhatdifferent services which may explain partially the difference; in any caseit is clear that this is a prevalent problem.
State-of-the-art e-mail spam filters have been applied successfully to filter comment spam. Thomason [221] analyzes a collection of over Fighting Spam in User-Generated Content 6,000 comments (of which 78% are spam) and shows that DSPAM5 canreach a false positive rate of about 1% and a false negative rate of lessthan 0.5%.
Mishne et al. [167] proposed a content-based approach to filter com- ment spam. They observe that, while in the case of e-mail spam eachmessage should be analyzed independently in principle, in the case ofcomments there is a context which is the page and site where the com-ment is posted. Their method starts by computing two language mod-els: one for the original page in which the comment is posted, and onefor each comment. Next, a measure of distance between the languagemodels of the page and each comment is computed. This distance isbased on the difference between their language models, measured usingthe Kullback–Leibler divergence. Finally, a threshold in this distanceis used to discriminate between spam comments (larger distance) andlegitimate comments (smaller distance).
Given that both the page and the comment may be very short, the model of each one can be enriched by means of linked pages. Thus, thelanguage model for a page can be computed by taking into considera-tion the text of the pages being linked, and the language model for acomment can be computed including the text of the pages linked fromthe comment. This is useful given that most spam comments currentlyinclude out-links as they are aimed at influencing link-based ranking.
A link to an unrelated page to the one being commented on increasesthe distance between the language models and makes the commentmore likely to be spam.
The "nofollow" attribute
Because of the negative impact that comment spam was having on the blogosphere, in 2005 the major blog-ging services and software vendors, along with Google, Yahoo!, andMSN Search proposed a new link attribute called nofollow [156]. Anylink on a Web page having this form: indicates that the destination of that hyperlink should not be affordedany additional weight or ranking by search engines doing ranking of 7.3 Publicly-Writable Pages pages based on link analysis. This means that the link may be followedby a Web crawler, but discarded when computing, for instance, Page-Rank or HITS. The usage scenarios for this type of link are publicly-writable Web pages like Wikis or Blogs where users can post links;several applications for maintaining these types of sites by default addthe nofollow tag to links posted by untrusted users. The intention isto discourage spammers from posting links in such pages. By mid-2006,about 1% of all Web links had the nofollow attribute applied [63]. By2009 that fraction had grown to 2.7% [76], but almost three-quarters ofthose were links to other pages on the same site, suggesting that mostuse of nofollow was to control explicitly how authority flows, ratherthan simply to make user-generated content not affect authority cal-culations. To the best of our knowledge, no study has been releasedpublicly measuring the effectiveness of the nofollow tag, either as adeterrent (comment spam is still ubiquitous!) or as a useful signal forranking.
Opinion and Review Spam
There are broadly two types of spam reviews [112]. First, there arefalse reviews that deliberately try to mislead readers or automatic sys-tems by giving undeserving positive or negative opinions about a prod-uct. Second, there are non-reviews that contain spam and are basicallycases of spam in publicly-writable pages. A more refined classificationof review spam [111] includes the following classes: False reviews: containing misleading judgments portrayed as truthful. Detecting these in general requires a considerableamount of domain knowledge.
Positive false reviews: undeservedly positive opinions.
Negative false reviews: undeservedly negative opin-
Non-reviews: do not contain misleading judgments.
Advertisement: these are similar to spam in publicly-
writable pages.
Fighting Spam in User-Generated Content Other: questions, meta-comments, vandalism, etc.
These can be on purpose (as in the case of vandal-ism) or by mistake (as in the case of, e.g., a misplacedquestion).
Brand reviews: contain only statements about a brand, but not about a product. Again, this can be done by users simplyby mistake.
The detection mechanism presented by Jindal and Liu [111] uses textual features from the review as well as context information. Thecontext information includes the feedback from other users which canvote a review as helpful or unhelpful, user information such as statisticsabout the ratings she has provided in the past, and also informationabout the product being reviewed.
In a data set of 470 manually labeled reviews from Amazon product reviews, they report a very high accuracy (AUC 0.98) in separatingnon-reviews and brand reviews from legitimate reviews. Finding falsereviews is harder, even for humans. To detect a subset of the possi-bly false reviews, they use near-duplicate detection to gather a set ofsuspicious cases. They include, for instance, the same user-id postingrepeatedly the same review to different products, or different user-idsposting exactly the same review text; these are often false reviews.
In separating this class of false reviews from legitimate reviews, theyachieve AUC 0.78.
The study of false reviews is further deepened in [113] where also the ratings are taken into account. User ratings are numeric votes forquality of the product that go from 1 to 5, usually represented as anumber of "stars" in the user interface. An interesting finding is thatratings that are substantially lower (more negative) than the averagerating of a product are more likely to be spam reviews, than ratingsthat are more positive than the average.
Methods of trust propagation such as the ones discussed in Section 5 can be employed to score opinions according to how trusted are theusers producing them. One method of this type is the TrustWalkermethod by Jamali and Ester [108] in which reviews in a collaborativerecommendation platform are scored by trust.
7.4 Social Networks and Social Media Sites Social Networks and Social Media Sites
The growth of online social networks like Facebook, Twitter, andMySpace has dramatically increased the ability for users to find andcommunicate directly with more people. This power can also be usedfor illegitimate aims, such as phishing attacks, malware dissemination,and as expected, spam.
Social Network Trust
A first step toward fighting spam in social networks is to developmethods for computing the reputation of members. This is particu-larly needed because participants of online social networks often sharepersonal details of their lives, making it important that the people withwhom they connect be trustworthy.
The SocialTrust model by Caverlee et al. [47, 48] is a global trust function computed in a centralized manner (as defined in Section 5.1).
First, the core trust score for a user u is computed; it is a function of thecore trust of the "friends" of u, of the inferred quality of the connectionsof u with them, and of the explicit feedback ratings received by u.
Second, the SocialTrust score for a user u at time t is computed; it is alinear combination of the core trust of u at time t, of the derivative ofu's core trust with respect to time, and of the average of the SocialTrustscore of u in the past. The purpose is to mitigate the effect of userswho accumulate a good reputation over time, and then take advantageof that reputation to behave maliciously.
There are also models based on local trust computations, in which the trust of a user is computed from the perspective of another user, andnot on the entire network. They include among others the reputationsystem implemented in the Advogato community, based on maximumflows [142], a model based on weighted paths due to Mui et al. [172]and Appleseed, a system based on spreading activation [255].
Social network trust can also be used across different services. The FaceTrust method by Sirivianos et al. [213] provides a general mecha-nism for verifying the credentials of a user. The idea is that if a useru needs to prove to a third party that the user has a certain property(e.g., that u is above 18 years old), the user may direct this third party Fighting Spam in User-Generated Content to a social network, which in turns can ask u's connections to validatethis assertion. This can be done in a privacy-preserving way, in whichdata about u that is not necessary for the transaction with the thirdparty to take place, and does not need to be disclosed outside the socialnetwork. Naturally, this can be extended to create an environment inwhich users can assure new online services that they are "good neti-zens" by providing credentials from their previous activities in othersocial networks.
Social Media Spam
Social media sites such as Delicious, YouTube, and Flickr, which allowposting of items or shared bookmarks, are prone to various types ofspam. Users can post or bookmark spammy content, or send it to otherusers when a direct user-to-user communication mechanism is available.
The mechanisms for user voting and reporting of abuse and spam canhelp reduce the impact of such spam, unless users collude by agreeingto promote or demote certain items through tags or votes. In any case,automatic detection of abusive of spammy items may help discouragespammers and improve the user experience for the whole community.
Tagging spam
Koutrika et al. [132, 133] introduce a model for malicious (spammy) and normal behavior of users in tagging systems.
According to their model, each object (photo, page, etc.) d posted to thesocial media site can be described by some tags S(d), while some mali-cious users pick tags in S(d)C as descriptors for d. Given that determin-ing S(d) requires domain knowledge, a spam-resistant tagging systemconsiders the matches of the tags of one user with the rest of the usersin the system as a measure of reliability for that user. Specifically, everytime two users assign the same tag to the same object, the reliabilityof both users increases; and this reliability is used when computing thestrength of the assignment of a task to an object. Their paper includesexperiments in a simulated tagging environment in which malicioususers operate either individually or as a group.
Abnormal tagging patterns can be detected, e.g., by graphical methods. Neubauer et al. [174] consider a graph in which users are 7.4 Social Networks and Social Media Sites connected if the number of items they have both tagged exceeds a cer-tain threshold. Coalitions of spammers may appear as large connectedcomponents (that are separated from the largest connected compo-nent) in this graph. In other words, groups of users tagging documentsin which the majority of legitimate users are not interested, are morelikely to be spammers.
Krause et al. [134] study social bookmarking systems, in particular the case of Bibsonomy.6 Their paper focuses on the characterization ofusers that contribute spam content to the system. These users are rep-resented by vectors containing features extracted from their user profile(e.g., number of digits in their names or e-mail addresses), from theirnetwork location (e.g., number of users sharing the same IP address ordomain), and from the tags they use (e.g., checking the intersection oftheir tags with a blacklist of known tags used by spammers). Puttingthese features together, they are able to build a classifier which achievesa high accuracy (AUC 0.93) on a test set of about 2,700 unseen cases.
The problem of finding these anti-social users in social media sites motivated the ECML/PKDD 2008 discovery challenge.7 In thatcompetition, a data set from Bibsonomy was provided including human-annotations for 22,000 spam users and 2,000 non-spam users. The win-ning entry by Gkanogiannis and Kalamboukis [82] represented eachuser by a document that was the concatenation of all their postingsin the system; then they used their text classification algorithm [81]obtaining an AUC of 0.98 in a test set of unseen cases. The runner-upentry by Gramme and Chevalier [86] used their RANK modeling toolover a richer feature set computed from the text, tags, resources postedby users; they obtained an AUC of 0.97 over the same test set.
Markines et al. [155] also consider the problem of spam within social bookmarking systems and consider characteristics of all three aspectsof a social bookmark: the tag, the user, and the target. They offer sixfeatures for this task: probability of a tag being used by a spammer,dissimilarity of tags used in a post, the likelihood of a target page beinggenerated automatically, the number of ads on the target page, the Fighting Spam in User-Generated Content likelihood of the content of the target page being plagiarized, and thefraction of a user's posts that still refer to valid resources. Each of thesefeatures is demonstrated to be useful, and the combination along witha classifier like AdaBoost provides quite competitive performance.
An application exists that uses simple heuristic to filter a stream of posts from the microblogging platform Twitter. The Clean Tweetsextension for Firefox hides posts from accounts that are less than oneday old or that contain too many tags.8 Voting spam
Bian et al. [26] study the effect of voting spam in the Yahoo! Answers9 question-answering portal. In this Web site, userspost questions and answers and vote on the answers of others eitherwith positive ("thumbs up") or negative ("thumbs down") votes. Theauthors introduce two synthetic attack models, one in which a set ofusers picks a set of answers and vote positively on them, and anotherin which a set of users pick a set of answers and vote positively onthem and negatively on the other answers to the same questions of theanswers being promoted. In both cases, the rankings are affected bythe introduction of spam votes; a ranking system can be "hardened"against spam by introducing synthetic votes following a certain attackmodel in the training set. In practice, this "teaches" the ranking systemto reduce the weights of the features that are affected by spam (e.g.,number of positive or negative votes) and thus reduces the impact ofspam at run time.
Tran et al. [223] describe a voting method that analyzes the social network of users, and gives less weight to votes from users that are notwell connected to other users. The authors show results from prelimi-nary experiments in Digg10 indicating that this method is effective indemoting highly ranked spammy content.
Video spam
Spam also plagues social video sites such as YouTube, in which users can post responses to existing videos. Benevenuto et al. [22]target video spammers in their work by examining attributes of objects, 10 7.5 Conclusions users, and the social network connecting them. They showed that spamusers and objects had distributions that differed from legitimate usersand videos for characteristics such as number of friends, number ofresponses received, number of favorites, etc.
While since the beginning of the Web, user-generated content hasplayed a central role, over time the Web has become even more inter-active. Content is generated not only by dedicated authors, but evencasual Web surfers are now participants in discussion boards, socialnetworks, photo and bookmark sharing sites, and more. We are com-municating and collaborating across many social environments, eachof which has the potential to be manipulated by malicious partici-pants of varying sophistication. A number of studies have examineddetection and amelioration approaches under simulated adversaries(e.g., [47, 132, 133]) while a few have used labeled data from real ser-vices (e.g., [22, 82, 155]). We also highlighted difficulties of the vocifer-ous subset of the Web called the blogosphere, as the size, popularity andease of creation of blog content has made such participatory contentboth valuable and easily affected by adversaries wishing to manipulatethe system.
Successful spam detection approaches for user-generated content have significant parallels to those methods described in earlier sections.
By modeling the entities involved, it is often possible to find featuresthat have different characteristics for spam versus non-spam contentor creators. The explicit modeling of author reputation can be helpfulin estimating entity reputation, and finally, training data to train andexploit automated classifiers is as always crucial.
Given our coverage of the various spamming mechanisms and the meth-ods to detect them, in this final section we discuss our views on thestatus of research in adversarial Web search. The struggle between thesearch engines and the spammers continues, with searchers and contentproviders often suffering from collateral damage. The continued diffi-culties, however, lend themselves as ongoing research problems, and anumber of resources are available to those interested in pursuing suchtopics.
The (Ongoing) Struggle Between Search
Engines and Spammers

Search Engine Perspective
Over time, search engines have developed sophisticated algorithms tofight Web spam. Indeed, one of the original motivations behind Page-Rank [183] was trying to counter content spam, by adding featuresto the ranking function of the search engine that were not under thecontrol of the author of the Web page itself. However, much like anyother feature, PageRank is manipulated by Web page authors trying 8.1 The (Ongoing) Struggle Between Search Engines and Spammers to deceive search engines. This is the reason why most search engines,while not publishing the exact details on their ranking functions, arebelieved to use hundreds or even thousands of different signals for rank-ing pages. Also, search engines use a variety of "penalties" once theydetect a spammer. These penalties can range from demotion of thepage to its removal from the search index. Again, the specific condi-tions under which these penalties are applied are not disclosed.
A part from efforts from individual search engines, the search engine industry has been able to achieve consensus in key areas in the past —such as sitemaps,1 and robot exclusion [130] — indicating that it is pos-sible to agree on industry-wide efforts for dealing with Web spam. Oneexample is the nofollow tag described in Section 7. Another examplerelated to search engine optimization is the recent introduction of thecanonical URL tag [137] to eliminate self-created duplicate content insearch engine indexes.
In the end, search engine providers want to provide the most useful and valuable service possible, which typically means they want to pro-vide an objective ranking of relevant results. Generally speaking, thismeans they would like Web site owners to optimize for users, not forsearch engines.
Search engines may be able to change the economics of spamming bymaking it more expensive and less profitable for spammers to spam.
However, even if the chances of suceeding become very small, therewill always be some people and organizations that will try to increasetheir audience in the short term by spamming.
As in e-mail spam, there is a general trend toward increasing sophis- tication of Web spam, but there is also a mixture of sophisticated andna¨ıve Web spam, possibly due to some new spammers trying old tacticsthat are well known by search engines and that can do little harm totheir ranking methods.
Also as in e-mail spam, sometimes the spam companies may sue the companies or groups that blacklist them. A high-profile case was the lawsuit by [188], which was as unsuccessful asprevious attempts by e-mail spammers to obtain legal protection fortheir activity. Google has maintained the position in US courts thattheir rankings of Web sites are opinions about those sites and as suchare protected by the First Amendment.2 Unsuspecting Web site owners are often hurt by the adversarial situ-ation in Web IR. If owners create high-quality original content, thatcontent may be copied and re-used by spammers. When a spammer'sWeb page rises and legitimate sites subsequently fall in rankings, con-tent providers may be sorely tempted to go beyond SEO and utilizespamming techniques to be competitive.
However, SEO experts such as Moran and Hunt [169] advise Web site administrators to refrain from doing spam: "Many unethical searchmarketing techniques (known as spam) try to fool search engines to findyour pages when they really should not match, and every search enginetakes measures to avoid being fooled." The main reason for SEOs to avoid spamming is the risk of being detected, either immediately or in the future: "The search engines are aware of the many sneaky waysthat site owners try to achieve undeserved ranks. . Ifthey discover that you're trying to do this, your site maybe penalized: Your rank may be downgraded, or yourpage — or even your whole site — could be banned.
Even if your site is never caught and punished, it's verylikely, we dare say inevitable, that your tricky techniquewill eventually stop working." [88].
"[I]f search engines do not flag [some spam] pagestoday, some day they will. They get smarter every year.
Beyond search engine smarts, over-optimized pages alsoleave you vulnerable to being reported by your com-petitors to search engines for spamming — causing a 8.2 Outlook human editor to check the page and possibly ban yoursite." [169].
While a high-profile site that gets cleaned up might be able to be removed from a blacklist fairly quickly (e.g., BMW [105]), most sitesmay find recovering from blacklisting actions to be difficult and time-consuming.
Spam is likely to continue to be an issue for search engines as bothspammers and search engines develop more powerful techniques. Theability to communicate at a low cost without any approval from a thirdparty has been key to the success of the Web and is unlikely to changein the future.
According to Metaxas and Destefano [164] Web spam is mostly a social problem, not a technical one; and as a social problem, the solu-tion is on the hands of people who are the objective of the spammer'sattempt. Accordingly, Gy¨ ongyi and Garcia-Molina state that: "[I]n the long run, the best solution to the ongoing bat-tle is to make spamming ineffective — not only in itsattempt to subvert search engine algorithms but also —and more important — in its attempt to coerce users. Ifpeople are more conscious about spamming and avoidbeing lured into its traps, the economic or social incen-tive for spamming will decrease." [92].
From a technical perspective, the arms race between search engine spammers and search engines does not need to continue indefinitely: "Victory does not require perfection, just a rate ofdetection that alters the economic balance for a would-be spammer." [182].
Note that in both cases, Web spammers need to understand that the situation has changed, or they will still generate spam, even when it isineffective (as in the case of comment spam when nofollow is applied).
Thus, it is the spammer's perception of the relative benefits and draw-backs of spam that needs to change in order for Web spam to truly end.
Web spam may, in the long term, become a smaller piece of the efforts related to Adversarial IR on the Web, as other problems maybecome more important over time. Bloggers and other enthusiasts aregenerating vast amounts of content and competing with each other toget the attention of users, and in this competition some of them resortto deceptive practices. Social media sites allowing a more direct one-to-one communication are popular and susceptible to be spammed.
Data Sets
Over the years, a few annotated corpora have been made available tothe research community for research on Web spam.
The Webb Spam Corpus3 [233] is a collection of 350,000 Web spam pages. It was built semi-automatically starting from a large corpusof e-mail spam messages, and scanning for URLs mentioned in thosemessages. The collection includes the contents of all the Web spampages, and it can be freely downloaded.
The Splog Blog Dataset4 [125] is a set of 3,000 blog home pages, out of which 700 have been labeled manually as splogs and 700 as legitimateblogs. The collection includes the contents of all the blog home pagesand the labels, and it can be freely downloaded.
The WEBSPAM-UK2006 and WEBSPAM-UK2007 data sets5 [43] are a set of hosts from a crawl of the .uk domain labeled by an inter-national team of volunteer researchers. In the newest collection, thereare 114,529 hosts out of which 6,479 have been labeled. The collectionincludes the contents of up to 400 pages per host (a larger version isavailable), the links between the hosts, and the labels. The collectionis freely available for download, except for the page contents that are 8.3 Research Resources available for download upon signing a research-only agreement. It wasused in the Web Spam Challenge 2007 and 2008.6 The 2008 ECML PKDD Discovery Challenge Dataset7 is a col- lection of bookmarks in a social bookmarking service (Bibsonomy).
It contains a few hundred thousand bookmarks (URLs or BibTeX files).
The operators of the bookmarking service have identified about 25,000accounts as spammers by manually inspecting bookmarks in the site.
The collection includes users, bookmarks, and labels, and is freely avail-able for download.
The Clue Web09 Dataset8 contains more than a billion Web pages and has been used in TREC since 2009. Gordon Cormack and collabo-rators at the University of Waterloo has built a classifier from honeypotqueries, labels from the WEBSPAM-UK2006 and WEBSPAM-UK2007collections, and a small set of hand-labeled data [56] and evaluated itin terms of the effect on retrieval. The labels produced have been madepublicly available.9 The 2010 ECML PKDD Discovery Challenge10 includes tasks related to Web host quality prediction for Internet Archives.
Query Logs
With respect to usage data, in general query logs are used by searchengine companies but are difficult to obtain and are not easily availablefor the academic community, mostly because of privacy issues as theyare hard to sufficiently anonymize them without degrading them sub-stantially, although research continues in that direction, e.g., [136, 129].
AOL released in 2006 a query log in what became a highly pub- licized incident. User privacy was compromised because of insufficientanonymization of the query log, causing a major uproar that resultedin the employees involved in the sharing of the query log being fired.
This incident and its implications are described in [211]. The query log was officially withdrawn by its authors, but it continues to be usedfor research on query log mining for several reasons. First, while it isno longer available for download from the AOL site, copies of it areavailable elsewhere11; second, it is assumed that research on this querylog may help avoid future privacy issues for users; and third, becausebeing a widely available data source, it allows researchers to reproduceand compare their approaches.
One main issue with the AOL query log was that it was freely available without any formal agreement from people receiving the querylog. During the WSCD 2009 workshop, Microsoft made available asample of their query log12 under a research-only agreement in whichresearchers had to assure, among other things, that they would notattempt to use the query log to uncover private information about anyuser, nor reveal any of the contents of the query log to third parties.
Finally, we note that an academic effort led by faculty from Carnegie Mellon University and the University of Massachusetts, Amherst hasrecently begun to collect query and usage logs from volunteers.13 Adversarial Information Retrieval on the Web14 is a series of yearlyworkshops started in 2005. This workshop continued as a joint AIR-Web/WICOW workshop on Web Quality in 2011.15 The topics ofthese workshops are closely related to those in this monograph. Otherresearch workshops related to the topics covered in this monograph arethe Conference on E-mail and Anti-Spam CEAS16 and broader venuessuch as the World Wide Web Conference.17 In addition to the aforementioned resources, there is a low-volume, announcements only mailing list with respect to Adversarial IR on theWeb.18 11 See for a list of mirrors.
16 8.4 Conclusions By now it should be apparent that there is no panacea for either sidein adversarial information retrieval, and that new opportunities forspam continue to appear as the Web continues to evolve into a moreparticipatory form. While decidedly less than ideal for searchers andcontent owners caught in the crossfire, this scenario bodes well for thoseemployed on both sides of the battle.
Carlos Castillo was partially supported by the Spanish Centre forthe Development of Industrial Technology under the CENIT pro-gram, project CEN-20101037, "Social Media" (
Brian Davison was partially supported by the U.S. National Science Foundation under Grant Number IIS-0545875.
[1] B. A, K. Csalog´ any, and T. Sarl´ os, "Link-based similarity search to fight Web spam," in Proceedings of the Second International Workshop on AdversarialInformation Retrieval on the Web (AIRWeb), 2006.
[2] J. Abernethy and O. Chapelle, "Semi-supervised classification with hyper- links," in Proceedings of the ECML/PKDD Graph Labeling Workshop,September 2007.
[3] J. Abernethy, O. Chapelle, and C. Castillo, "Webspam identification through content and hyperlinks," in Proceedings of the Fourth International Work-shop on Adversarial Information Retrieval on the Web (AIRWEB), pp. 41–44,ICPS: ACM Press, April 2008.
[4] J. Abernethy, O. Chapelle, and C. Castillo, "Graph regularization methods for web spam detection," Machine Learning Journal, vol. 81, no. 2, pp. 207–225,2010.
[5] S. Adali, T. Liu, and M. Magdon-Ismail, "Optimal link bombs are uncoor- dinated," in Proceedings of the First International Workshop on AdversarialInformation Retrieval on the Web (AIRWeb), May 2005.
[6] B. T. Adler and L. de Alfaro, "A content-driven reputation system for the Wikipedia," in Proceedings of the 16th International Conference on WorldWide Web (WWW), pp. 261–270, New York, NY, USA: ACM, 2007.
[7] E. Amitay, S. Yogev, and E. Yom-Tov, "Serial sharers: Detecting split identi- ties of Web authors," in Workshop on Plagiarism Analysis, Authorship Iden-tification, And Near-Duplicate Detection, July 2007.
[8] R. Andersen, C. Borgs, J. Chayes, J. Hopcraft, V. Mirrokni, and S.-H. Teng, "Local computation of PageRank contributions," in Algorithms and Models for the Web-Graph, vol. 4863 of Lecture Notes in Computer Science, pp. 150–165, Springer, 2007.
[9] A. Arasu, J. Cho, H. Garcia-Molina, A. Paepcke, and S. Raghavan, "Searching the Web," ACM Transactions on the Internet Technology (TOIT) 1, vol. 1,pp. 2–43, August 2001.
[10] J. Attenberg and T. Suel, "Cleaning search results using term distance fea- tures," in Proceedings of the Fourth International Workshop on AdversarialInformation Retrieval on the Web (AIRWeb), pp. 21–24, New York, NY, USA:ACM, 2008.
[11] V. Bacarella, F. Giannotti, M. Nanni, and D. Pedreschi, "Discovery of ads Web hosts through traffic data analysis," in Proceedings of the 9th ACM SIG-MOD Workshop on Research Issues in Data Mining and Knowledge Discovery(DMKD), pp. 76–81, New York, NY, USA: ACM, 2004.
[12] R. Baeza-Yates, C. Castillo, and V. L´ opez, "PageRank increase under dif- ferent collusion topologies," in First International Workshop on AdversarialInformation Retrieval on the Web (AIRWeb), pp. 17–24, May 2005.
[13] R. Baeza-Yates and B. Ribeiro-Neto, "Modern Information Retrieval," Addi- son Wesley, May 1999.
[14] J. Bar-Ilan, "Web links and search engine ranking: The case of Google and the query "jew"," Journal of the American Society for Information Scienceand Technology, vol. 57, no. 12, pp. 1581–1589, 2006.
[15] J. Bar-Ilan, "Google bombing from a time perspective," Journal of Computer- Mediated Communication, vol. 12, no. 3, 2007.
[16] Z. Bar-Yossef, I. Keidar, and U. Schonfeld, "Do not crawl in the DUST: Dif- ferent URLs with similar text," ACM Transactions on the Web, vol. 3, no. 1,pp. 1–31, 2009.
[17] J. Battelle, The Search: How Google and Its Rivals Rewrote the Rules of Busi- ness and Transformed Our Culture. New York: Portfolio, 2005.
[18] L. Becchetti, C. Castillo, D. Donato, R. Baeza-Yates, and S. Leonardi, "Link analysis for Web spam detection," ACM Transactions on the Web, vol. 2,no. 1, pp. 1–42, February 2008.
[19] L. Becchetti, C. Castillo, D. Donato, S. Leonardi, and R. Baeza-Yates, "Using rank propagation and probabilistic counting for link-based spam detection,"in Proceedings of the Workshop on Web Mining and Web Usage Analysis(WebKDD), ACM Press, August 2006.
[20] A. A. Bencz´ any, and M. Uher, "Detecting nepotistic links by language model disagreement," in Proceedings of the 15th InternationalConference on World Wide Web (WWW), pp. 939–940, ACM Press, 2006.
[21] A. A. Bencz´ os, and M. Uher, "SpamRank: Fully auto- matic link spam detection," in Proceedings of the First International Workshopon Adversarial Information Retrieval on the Web (AIRWeb), May 2005.
[22] F. Benevenuto, T. Rodrigues, V. Almeida, J. Almeida, C. Zhang, and K. Ross, "Identifying video spammers in online social networks," in Proceedings of theFourth International Workshop on Adversarial Information Retrieval on theWeb (AIRWeb), pp. 45–52, New York, NY, USA: ACM, 2008.
[23] P. Berkhin, "A survey on PageRank computing," Internet Mathematics, vol. 2, no. 2, pp. 73–120, 2005.
[24] K. Berlt, E. S. de Moura, C. M. Andr´ e, N. Ziviani, and T. Couto, "A hyper- graph model for computing page reputation on Web collections," in Pro-ceedings of the Simp´ osio Brasileiro de Banco de Dados (SBBD), pp. 35–49, October 2007.
[25] K. Bharat and M. R. Henzinger, "Improved algorithms for topic distillation in hyperlinked environments," in Proceedings of the 21st International ACMSIGIR Conference on Research and Development in Information Retrieval,pp. 104–111, August 1998.
[26] J. Bian, Y. Liu, E. Agichtein, and H. Zha, "A few bad votes too many?: Towards robust ranking in social media," in Proceedings of the Fourth Interna-tional Workshop on Adversarial Information Retrieval on the Web (AIRWeb),pp. 53–60, New York, NY, USA: ACM, 2008.
[27] A. Bifet, C. Castillo, P.-A. Chirita, and I. Weber, "An analysis of factors used in search engine ranking," in Proceedings of the First International Workshopon Adversarial Information Retrieval (AIRWeb), May 2005.
o, and A. Bencz´ ur, "Linked latent dirichlet allocation in Web spam filtering," in Proceedings of the 5th International Workshop onAdversarial Information Retrieval on the Web (AIRWeb), pp. 37–40, ACMPress, 2009.
o, and A. A. Bencz´ ur, "Latent dirichlet allocation in Web spam filtering," in Proceedings of the 4th International Workshop on AdversarialInformation Retrieval on the Web (AIRWeb), pp. 29–32, New York, NY, USA:ACM, 2008.
[30] D. M. Blei, A. Y. Ng, and M. I. Jordan, "Latent dirichlet allocation," Journal of Machine Learning Research, vol. 3, pp. 993–1022, 2003.
[31] A. Borodin, G. O. Roberts, J. S. Rosenthal, and P. Tsaparas, "Finding author- ities and hubs from link structures on the World Wide Web," in Proceedings ofthe 10th International Conference on World Wide Web (WWW), pp. 415–429,2001.
[32] O. P. Boykin and V. Roychowdhury, "Personal Email networks: an effective anti-spam tool," Condensed Matter cond-mat/0402143, 2004.
[33] S. Brin, R. Motwani, L. Page, and T. Winograd, "What can you do with a Web in your pocket?," Data Engineering Bulletin, vol. 21, no. 2, pp. 37–47,1998.
[34] S. Brin and L. Page, "The anatomy of a large-scale hypertextual Web search engine," in Proceedings of the 7th International Conference on the World WideWeb, pp. 107–117, April 1998.
[35] A. Brod and R. Shivakumar, "Advantageous semi-collusion," The Journal of Industrial Economics, vol. 47, no. 2, pp. 221–230, 1999.
[36] A. Z. Broder, S. C. Glassman, M. S. Manasse, and G. Zweig, "Syntactic clus- tering of the Web," Computer Networks and ISDN Systems, vol. 29, no. 8–13,pp. 1157–1166, September 1997.
[37] T. A. Brooks, "Web search: How the Web has changed information retrieval," Information Research, vol. 8, no. 3, April 2003.
[38] G. Buehrer, J. W. Stokes, and K. Chellapilla, "A large-scale study of auto- mated Web search traffic," in Proceedings of the 4th International Workshopon Adversarial Information Retrieval on the Web (AIRWeb), pp. 1–8, NewYork, NY, USA: ACM, 2008.
uttcher, C. L. A. Clarke, and B. Lushman, "Term proximity scoring for ad-hoc retrieval on very large text collections," in Proceedings of the 29thACM Annual SIGIR Conference on Research and Development in InformationRetrieval, pp. 621–622, New York, NY, USA: ACM Press, 2006.
[40] C. Castillo, "Effective Web Crawling," PhD thesis, University of Chile, 2004.
[41] C. Castillo, C. Corsi, D. Donato, P. Ferragina, and A. Gionis, "Query log mining for detecting polysemy and spam," in Proceedings of the KDD Work-shop on Web Mining and Web Usage Analysis (WEBKDD), Springer: LNCS,August 2008.
[42] C. Castillo, C. Corsi, D. Donato, P. Ferragina, and A. Gionis, "Query-log mining for detecting spam," in Proceedings of the 4th International Workshopon Adversarial Information Retrieval on the Web (AIRWeb), ICPS: ACMPress, April 2008.
[43] C. Castillo, D. Donato, L. Becchetti, P. Boldi, S. Leonardi, M. Santini, and S. Vigna, "A reference collection for Web spam," SIGIR Forum, vol. 40, no. 2,pp. 11–24, December 2006.
[44] C. Castillo, D. Donato, A. Gionis, V. Murdock, and F. Silvestri, "Know your neighbors: Web spam detection using the web topology," in Proceedings of the30th Annual International ACM SIGIR Conference on Research and Devel-opment in Information Retrieval, ACM, July 2007.
[45] J. Caverlee, "Tamper-Resilient Methods for Web-Based Open Systems," PhD thesis, College of Computing, Georgia Institute of Technology, August 2007.
[46] J. Caverlee and L. Liu, "Countering Web spam with credibility-based link analysis," in Proceedings of the Twenty-Sixth Annual ACM Symposium onPrinciples of Distributed Computing (PODC), pp. 157–166, New York, NY,USA: ACM, 2007.
[47] J. Caverlee, L. Liu, and S. Webb, "Socialtrust: Tamper-resilient trust estab- lishment in online communities," in Proceedings of the 8th ACM/IEEE-CSJoint Conference on Digital Libraries (JCDL), pp. 104–114, 2008.
[48] J. Caverlee, L. Liu, and S. Webb, "Towards robust trust establishment in Web-based social networks with SocialTrust," in Proceedings of the 17th Inter-national World Wide Web Conference (WWW), pp. 1163–1164, ACM, 2008.
[49] J. Caverlee, S. Webb, and L. Liu, "Spam-resilient Web rankings via influence throttling," in Proceedings of the IEEE International Parallel and DistributedProcessing Symposium (IPDPS), pp. 1–10, 2007.
[50] K. Chellapilla and D. M. Chickering, "Improving cloaking detection using search query popularity and monetizability," in Proceedings of the 2nd Interna-tional Workshop on Adversarial Information Retrieval on the Web (AIRWeb),pp. 17–24, August 2006.
[51] K. Chellapilla and A. Maykov, "A taxonomy of JavaScript redirection spam," in Proceedings of the 3rd International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), pp. 81–88, New York, NY, USA: ACM Press,2007.
[52] A. Cheng and E. Friedman, "Manipulability of PageRank under sybil strate- gies," in Proceedings of the First Workshop on the Economics of NetworkedSystems (NetEcon06), 2006.
[53] Y.-J. Chung, M. Toyoda, and M. Kitsuregawa, "A study of link farm distri- bution and evolution using a time series of Web snapshots," in Proceedings ofthe 5th International Workshop on Adversarial Information Retrieval on theWeb (AIRWeb), pp. 9–16, ACM Press, 2009.
[54] A. Clausen, "The cost of attack of PageRank," in Proceedings of the Inter- national Conference on Agents, Web Technologies and Internet Commerce(IAWTIC), July 2004.
[55] G. V. Cormack, "Email spam filtering: A systematic review," Foundations and Trends in Information Retrieval 1, pp. 335–455, 2008.
[56] G. V. Cormack, M. Smucker, and C. L. Clarke, "Efficient and effective spam filtering and re-ranking for large web datasets," Unpublished draft, availablefrom, retrieved10, April 2010.
[57] N. Craswell, O. Zoeter, M. Taylor, and B. Ramsey, "An experimental compar- ison of click position-bias models," in Proceedings of the First InternationalConference on Web Search and Data Mining (WSDM), pp. 87–94, New York,NY, USA: ACM, 2008.
[58] B. Croft, D. Metzler, and T. Strohman, Search Engines: Information Retrieval in Practice. Addison Wesley, 2009.
[59] A. L. C. da Costa-Carvalho, P.-A. Chirita, E. S. de Moura, P. Calado, and W. Nejdl, "Site level noise removal for search engines," in Proceedings of the15th International Conference on World Wide Web (WWW), pp. 73–82, NewYork, NY, USA: ACM Press, 2006.
[60] N. Dai, B. Davison, and X. Qi, "Looking into the past to better classify Web spam," in Proceedings of the 5th International Workshop on Adversarial Infor-mation Retrieval on the Web (AIRWeb), pp. 1–8, ACM Press, 2009.
[61] N. Daswani and M. Stoppelman, "The anatomy of Clickbot.A," in Proceedings of the USENIX HOTBOTS Workshop, April 2007.
[62] B. D. Davison, "Recognizing nepotistic links on the Web," in Artificial Intel- ligence for Web Search, pp. 23–28, AAAI Press, July 2000.
[63] B. D. Davison, M. Najork, and T. Converse, "Adversarial information retrieval on the Web (AIRWeb 2006)," SIGIR Forum, vol. 40, no. 2, pp. 27–30, 2006.
[64] J. Douceur, "The sybil attack," in Proceedings of the First International Peer To Peer Systems Workshop (IPTPS), pp. 251–260, Springer: Vol. 2429 ofLecture Notes in Computer Science, January 2002.
[65] I. Drost and T. Scheffer, "Thwarting the nigritude ultramarine: Learning to identify link spam," in Proceedings of the 16th European Conference onMachine Learning (ECML), pp. 233–243, Vol. 3720 of Lecture Notes in Arti-ficial Intelligence, 2005.
[66] Y. Du, Y. Shi, and X. Zhao, "Using spam farm to boost PageRank," in Proceedings of the 3rd International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), pp. 29–36, New York, NY, USA: ACM,2007.
[67] O. Duskin and D. G. Feitelson, "Distinguishing humans from robots in Web search logs: Preliminary results using query rates and intervals," in Proceedingsof the WSDM Workshop on Web Search Click Data (WSCD), pp. 15–19, NewYork, NY, USA: ACM, 2009.
[68] M. Egele, C. Kruegel, and E. Kirda, "Removing web spam links from search engine results," Journal in Computer Virology, In press. Published online 22August, 2009.
[69] N. Eiron, K. S. Curley, and J. A. Tomlin, "Ranking the Web frontier," in Pro- ceedings of the 13th International Conference on World Wide Web, pp. 309–318, New York, NY, USA: ACM Press, 2004.
[70] E. Enge, "Matt cutts interviewed by eric enge," Article online at http://www. and retrieved on11 April 2010, March 2010.
elyi, A. A. Bencz´ ur, J. Masanes, and D. Sikl´ osi, "Web spam filtering in internet archives," in Proceedings of the 5th International Workshop onAdversarial Information Retrieval on the Web (AIRWeb), pp. 17–20, ACMPress, 2009.
[72] J. Feigenbaum, S. Kannan, M. A. McGregor, S. Suri, and J. Zhang, "On graph problems in a semi-streaming model," in Proceedings of the 31st InternationalColloquium on Automata, Languages and Programming (ICALP), pp. 531–543, Springer: Vol. 3142 of LNCS, 2004.
[73] D. Fetterly, "Adversarial Information Retrieval: the manipulation of Web con- tent," ACM Computing Reviews, July 2007.
[74] D. Fetterly, M. Manasse, and M. Najork, "Spam, damn spam, and statistics: Using statistical analysis to locate spam Web pages," in Proceedings of theSeventh Workshop on the Web and databases (WebDB), pp. 1–6, June 2004.
[75] D. Fetterly, M. Manasse, and M. Najork, "Detecting phrase-level duplica- tion on the World Wide Web," in Proceedings of the 28th Annual Interna-tional ACM SIGIR Conference on Research and Development in InformationRetrieval, pp. 170–177, New York, NY, USA: ACM, 2005.
[76] R. Fishkin, "Lessons learned building an index of the WWW," Retrieved 15 June 2009 from, April 2009.
[77] S. Fox, M. Madden, and A. Smith, "Digital footprints," Pew Internet and American Life report. Retrieved 15 June 2009 from http://www.pewinternet.
org/Reports/2007/Digital-Footprints.aspx, December 2007.
[78] Q. Gan and T. Suel, "Improving Web spam classifiers using link structure," in Proceedings of the 3rd International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), pp. 17–20, New York, NY, USA: ACM, 2007.
[79] D. Gibson, R. Kumar, and A. Tomkins, "Discovering large dense subgraphs in massive graphs," in Proceedings of the 31st International Conference on VeryLarge Data Bases (VLDB), pp. 721–732, VLDB Endowment, 2005.
[80] Y. Gil and D. Artz, "Towards content trust of Web resources," in Proceedings of the 15th International Conference on World Wide Web (WWW), pp. 565–574, New York, NY, USA: ACM, 2006.
[81] A. Gkanogiannis and T. Kalamboukis, "An algorithm for text categorization," in Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 869–870, New York,NY, USA: ACM, 2008.
[82] A. Gkanogiannis and T. Kalamboukis, "A novel supervised learning algorithm and its use for spam detection in social bookmarking systems," in Proceedingsof the ECML/PKDD Discovery Challenge, 2008.
[83] H. L. Gomes, B. R. Almeida, A. M. L. Bettencourt, V. Almeida, and M. J.
Almeida, "Comparative graph theoretical characterization of networks of spamand legitimate email," April 2005.
[84] M. Goodstein and V. Vassilevska, "A two player game to combat WebSpam," Technical Report, Carnegie Mellon University, 2007.
[85] M. Gori and I. Witten, "The bubble of Web visibility," Communications of the ACM, vol. 48, no. 3, pp. 115–117, March 2005.
[86] P. Gramme and J.-F. Chevalier, "Rank for spam detection — ECML discovery challenge," in Proceedings of the ECML/PKDD Discovery Challenge, 2008.
[87] A. L. Granka, T. Joachims, and G. Gay, "Eye-tracking analysis of user behav- ior in www search," in Proceedings of the 27th Annual International ACMSIGIR Conference on Research and Development in Information Retrieval,pp. 478–479, New York, NY, USA: ACM, 2004.
[88] J. Grappone and G. Couzin, Search Engine Optimization: An Hour a Day.
Wiley, 2006.
[89] R. Guha, R. Kumar, P. Raghavan, and A. Tomkins, "Propagation of trust and distrust," in Proceedings of the 13th International Conference on WorldWide Web (WWW), pp. 403–412, New York, NY, USA: ACM Press, 2004.
[90] D. M. Guinness, H. Zeng, Li, D. Narayanan, and M. Bhaowal, "Investigations into trust for collaborative information. repositories: A Wikipedia case study,"in Proceedings of workshop on Models of Trust for the Web (MTW06), May2006.
ongyi and H. Garcia-Molina, "Link spam alliances," in Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), pp. 517–528, 2005.
ongyi and H. Garcia-Molina, "Spam: It's not just for inboxes anymore," IEEE Computer Magazine, vol. 38, no. 10, pp. 28–34, 2005.
ongyi and H. Garcia-Molina, "Web spam taxonomy," in Proceedings of the First International Workshop on Adversarial Information Retrieval on theWeb (AIRWeb), pp. 39–47, May 2005.
ongyi, H. Garcia-Molina, and J. Pedersen, "Combating Web spam with TrustRank," in Proceedings of the 30th International Conference on VeryLarge Data Bases (VLDB), pp. 576–587, Morgan Kaufmann, August 2004.
ongyi, "Applications of Web link analysis," PhD thesis, Stanford Uni- versity, Adviser: Hector Garcia-Molina, 2008.
ongyi, P. Berkhin, H. Garcia-Molina, and J. Pedersen, "Link spam detection based on mass estimation," in Proceedings of the 32nd InternationalConference on Very Large Databases (VLDB), pp. 439–450, 2006.
[97] H. T. Haveliwala, "Topic-sensitive PageRank," in Proceedings of the Eleventh World Wide Web Conference (WWW), pp. 517–526, ACM Press, May 2002.
[98] R. M. Henzinger, R. Motwani, and C. Silverstein, "Challenges in Web search engines," SIGIR Forum, vol. 37, no. 2, 2002.
[99] R. Herbrich, T. Graepel, and K. Obermayer, "Large margin rank bound- aries for ordinal regression," in Advances in Large Margin Classifiers, (Smola,Bartlett, Schoelkopf, and Schuurmans, eds.), pp. 115–132, Cambridge, MA:MIT Press, 2000.
[100] A. Heydon and M. Najork, "Mercator: A scalable, extensible web crawler," World Wide Web, vol. 2, no. 4, pp. 219–229, 1999.
[101] P. Heymann, G. Koutrika, and H. Garcia-Molina, "Fighting spam on social Web sites: A survey of approaches and future challenges," IEEE Internet Com-puting, vol. 11, no. 6, pp. 36–45, 2007.
[102] J. Hopcroft and D. Sheldon, "Manipulation-resistant reputations using hitting time," in Proceedings of the Workshop on Algorithms and Models for the Web-Graph (WAW), pp. 68–81, Springer: Vol. 2863 of Lecture Notes in ComputerScience, December 2007. Also appears in Internet Mathematics 5, 5:71–90,2009.
[103] J. Hopcroft and D. Sheldon, "Network reputation games," Techical Report, Cornell University, October 2008.
[104] M. Hu, E.-P. Lim, A. Sun, W. H. Lauw, and B.-Q. Vuong, "Measur- ing article quality in Wikipedia: Models and evaluation," in Proceedings ofthe Sixteenth ACM Conference on Information and Knowledge Management(CIKM), pp. 243–252, New York, NY, USA: ACM, 2007.
[105] S. Hutcheon, "Google pardons BMW website," in Sydney Morning Herald, Retrieved 18 June 2009 from, February 2006.
[106] N. Immorlica, K. Jain, and M. Mahdian, "Game-theoretic aspects of design- ing hyperlink structures," in Proceedings of the 2nd Workshop on Internetand Network Economics (WINE), pp. 150–161, Vol. 4286, Springer LNCS,December 2006.
[107] N. Immorlica, K. Jain, M. Mahdian, and K. Talwar, "Click fraud resistant methods for learning click-through rates," in Proceedings of the Workshopon Internet and Network Economics (WINE), pp. 34–45, Springer, Berlin:Vol. 3828 of Lecture Notes in Computer Science, 2005.
[108] M. Jamali and M. Ester, "Trustwalker: A random walk model for combining trust-based and item-based recommendation," in Proceedings of the 15th ACMSIGKDD International Conference on Knowledge Discovery and Data Mining,pp. 397–406, New York, NY, USA: ACM, 2009.
[109] J. B. Jansen, "Click fraud," Computer, vol. 40, no. 7, pp. 85–86, 2007.
[110] Q. Jiang, L. Zhang, Y. Zhu, and Y. Zhang, "Larger is better: Seed selection in link-based anti-spamming algorithms," in Proceedings of the 17th Interna-tional Conference on World Wide Web (WWW), pp. 1065–1066, New York,NY, USA: ACM, 2008.
[111] N. Jindal and B. Liu, "Analyzing and detecting review spam," in Proceedings of the 7th IEEE International Conference on Data Mining (ICDM), pp. 547–552, 2007.
[112] N. Jindal and B. Liu, "Review spam detection," in Proceedings of the 16th International Conference on World Wide Web (WWW), pp. 1189–1190, NewYork, NY, USA: ACM Press, 2007.
[113] N. Jindal and B. Liu, "Opinion spam and analysis," in Proceedings of the International Conference on Web Search and Data Mining (WSDM), pp. 219–230, New York, NY, USA: ACM, 2008.
[114] T. Joachims, "Optimizing search engines using clickthrough data," in Pro- ceedings of the ACM Conference on Knowledge Discovery and Data Mining(KDD), pp. 133–142, New York, NY: ACM Press, 2002.
[115] T. Joachims, L. Granka, B. Pan, H. Hembrooke, and G. Gay, "Accurately interpreting clickthrough data as implicit feedback," in Proceedings of the 28thAnnual International ACM SIGIR Conference on Research and Developmentin Information Retrieval, pp. 154–161, New York, NY, USA: ACM Press,2005.
[116] T. Jones, D. Hawking, and R. Sankaranarayana, "A framework for measur- ing the impact of Web spam," in Proceedings of 12th Australasian DocumentComputing Symposium (ADCS), December 2007.
[117] T. Jones, D. Hawking, R. Sankaranarayana, and N. Craswell, "Nullification test collections for Web spam and SEO," in Proceedings of the 5th Interna-tional Workshop on Adversarial Information Retrieval on the Web (AIRWeb),pp. 53–60, New York, NY, USA: ACM, April 2009.
[118] T. Z. Jr., "Gaming the search engine, in a political season," in New York Times, November 2006.
[119] D. S. Kamvar, T. M. Schlosser, and H. Garcia-Molina, "The Eigentrust algo- rithm for reputation management in P2P networks," in Proceedings of the12th International Conference on World Wide Web (WWW), pp. 640–651,New York, NY, USA: ACM Press, 2003.
[120] G. Karypis and V. Kumar, "Multilevel k-way partitioning scheme for irregu- lar graphs," Journal of Parallel and Distributed Computation, vol. 48, no. 1,pp. 96–129, 1998.
[121] T. Katayama, T. Utsuro, Y. Sato, T. Yoshinaka, Y. Kawada, and T. Fukuhara, "An empirical study on selective sampling in active learning for Splogdetection," in Proceedings of the 5th International Workshop on AdversarialInformation Retrieval on the Web (AIRWeb), pp. 29–36, ACM Press, 2009.
[122] M. J. Kleinberg, "Authoritative sources in a hyperlinked environment," Jour- nal of the ACM, vol. 46, no. 5, pp. 604–632, 1999.
ohne, "Optimizing a large dynamically generated Website for search engine crawling and ranking," Master's thesis, Technical University of Delft,2006.
[124] P. Kolari, "Detecting Spam Blogs: An Adaptive Online Approach," PhD the- sis, Department of Computer Science and Electrical Engineering, Universityof Maryland-Baltimore County, 2007.
[125] P. Kolari, T. Finin, A. Java, and A. Joshi, "Splog blog dataset," Techical Report, UMBC ebiquity, 2006.
[126] P. Kolari, T. Finin, A. Java, and A. Joshi, "Towards spam detection at ping servers," in Proceedings of the International Conference on Weblogs and SocialMedia (ICWSM), AAAI Press. Demo, March 2007.
[127] P. Kolari, A. Java, and T. Finin, "Characterizing the Splogosphere," in Pro- ceedings of the 3rd Annual Workshop on the Weblogging Ecosystem, 2006.
[128] P. Kolari, A. Java, T. Finin, T. Oates, and A. Joshi, "Detecting spam blogs: A machine learning approach," in Proceedings of the National Conference onArtificial Intelligence (AAAI), July 2006.
[129] A. Korolova, K. Kenthapadi, N. Mishra, and A. Ntoulas, "Releasing search queries and clicks privately," in Proceedings of the 18th International Confer-ence on World Wide Web (WWW), pp. 171–180, New York, NY, USA: ACM,2009.
[130] M. Koster, "A standard for robot exclusion," robots.html, 1996.
[131] Z. Kou, "Stacked graphical learning," PhD thesis, School of Computer Science, Carnegie Mellon University, 2007.
[132] G. Koutrika, A. F. Effendi, Z. Gy¨ ongyi, P. Heymann, and H. Garcia-Molina, "Combating spam in tagging systems," in Proceedings of the 3rd Interna-tional Workshop on Adversarial Information Retrieval on the Web (AIRWeb),pp. 57–64, New York, NY, USA: ACM Press, 2007.
[133] G. Koutrika, A. F. Effendi, Z. Gy¨ ongyi, P. Heymann, and H. Garcia-Molina, "Combating spam in tagging systems: An evaluation," ACM Transactions onthe Web, vol. 2, no. 4, pp. 1–34, 2008.
[134] B. Krause, H. A. Schimitz, and G. Stumme, "The anti-social tagger — detect- ing spam in social bookmarking systems," in Proceedings of the Fourth Inter-national Workshop on Adversarial Information Retrieval on the Web (AIR-Web), April 2008.
[135] V. Krishnan and R. Raj, "Web spam detection with anti-TrustRank," in Proceedings of the 2nd International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), pp. 37–40, 2006.
[136] R. Kumar, J. Novak, B. Pang, and A. Tomkins, "On anonymizing query logs via token-based hashing," in Proceedings of the 16th International Conferenceon World Wide Web (WWW), pp. 629–638, New York, NY, USA: ACM Press,2007.
[137] J. Kupke and M. Ohye, "Specify your canonical," Retrieved 18 June 2009 canonical.html, February 2009.
[138] N. A. Langville and D. C. Meyer, "Deeper inside PageRank," Internet Math- ematics, vol. 1, no. 3, pp. 335–380, 2003.
[139] N. A. Langville and D. C. Meyer, Google's PageRank and Beyond: The Science of Search Engine Rankings. Princeton, NJ: Princeton University Press, 2006.
[140] H.-T. Lee, D. Leonard, X. Wang, and D. Loguinov, "Irlbot: Scaling to 6 billion pages and beyond," ACM Transactions on the Web, vol. 3, no. 3, pp. 1–34,2009.
[141] R. Lempel and S. Moran, "The stochastic approach for link-structure analysis (SALSA) and the TKC effect," Computer Networks, vol. 33, no. 1–6, pp. 387–401, 2000.
[142] R. Levien and A. Aiken, "Attack-resistant trust metrics for public key certifi- cation," in Proceedings of the 7th USENIX Security Symposium, pp. 229–242,1998.
[143] J. Lewis, "Google bombs," in LA Weekly, Retrieved June 1, 2009 from [144] G. Liang, "Surveying Internet usage and impact in five Chinese cities," Report of the Research Center for Social Development, Chinese Academy of Social Sci-ences. Retrieved 15 June 2009 from 02 06 china.pdf, November 2005.
[145] M. Lifantsev, "Voting model for ranking Web pages," in Proceedings of the International Conference on Internet Computing (IC), (P. Graham andM. Maheswaran, eds.), pp. 143–148, CSREA Press, June 2000.
[146] J.-L. Lin, "Detection of cloaked Web spam by using tag-based methods," Expert Systems with Applications, vol. 36, no. 4, pp. 7493–7499, May 2009.
[147] Y.-R. Lin, H. Sundaram, Y. Chi, J. Tatemura, and L. B. Tseng, "Splog detec- tion using self-similarity analysis on blog temporal dynamics," in Proceedingsof the 3rd International Workshop on Adversarial Information Retrieval onthe Web (AIRWeb), pp. 1–8, New York, NY, USA: ACM Press, 2007.
[148] Y.-R. Lin, H. Sundaram, Y. Chi, J. Tatemura, and L. B. Tseng, "Detecting splogs via temporal dynamics using self-similarity analysis," ACM Transationson the Web, vol. 2, no. 1, pp. 1–35, 2008.
[149] T. Liu, "Analyzing the importance of group structure in the Google Page- Rank algorithm," Master's thesis, Rensselaer Polytechnic Institute, November2004.
[150] Y. Liu, R. Cen, M. Zhang, S. Ma, and L. Ru, "Identifying Web spam with user behavior analysis," in Proceedings of the 4th International Workshop onAdversarial Information Retrieval on the Web (AIRWeb), pp. 9–16, New York,NY, USA: ACM, 2008.
[151] Y. Liu, B. Gao, T.-Y. Liu, Y. Zhang, Z. Ma, S. He, and H. Li, "Browse- Rank: Letting web users vote for page importance," in Proceedings of the 31stAnnual International ACM SIGIR Conference on Research and Developmentin Information Retrieval, pp. 451–458, New York, NY, USA: ACM, 2008.
[152] J. Ma, K. L. Saul, S. Savage, and M. G. Voelker, "Beyond blacklists: Learning to detect malicious web sites from suspicious urls," in Proceedings of the 15thACM SIGKDD International Conference on Knowledge Discovery and DataMining, pp. 1245–1254, New York, NY, USA: ACM, 2009.
[153] C. C. Mann, "How click fraud could swallow the internet," Wired, vol. 14, no. 1, January 2006.
[154] D. C. Manning, P. Raghavan, and H. Sch¨ utze, Introduction to Information Retrieval. Cambridge University Press, 2008.
[155] B. Markines, C. Cattuto, and F. Menczer, "Social spam detection," in Proceed- ings of the 5th International Workshop on Adversarial Information Retrievalon the Web (AIRWeb), pp. 41–48, New York, NY, USA: ACM, 2009.
[156] K. Marks and T. Celik, "Microformats: The rel=nofollow attribute," Techi- cal Report, Technorati, 2005.
Online at nofollow. Last accessed 29 January 2009.
[157] K. Marks and T. Celik, "Microformats: Vote links," Technical Report, Techno- rati, 2005. Online at http:// Last accessed29 January 2009.
[158] S. Marti and H. Garcia-Molina, "Taxonomy of trust: Categorizing P2P repu- tation systems," Computer Networks, vol. 50, no. 4, pp. 472–484, March 2006.
[159] J. Martinez-Romo and L. Araujo, "Web spam identification through language model analysis," in Proceedings of the 5th International Workshop on Adver-sarial Information Retrieval on the Web (AIRWeb), pp. 21–28, ACM Press,2009.
[160] K. Mason, "Detecting Colluders in PageRank: Finding Slow Mixing States in a Markov Chain," PhD thesis, Department of Engineering Economic Systemsand Operations Research, Stanford University, September 2005.
[161] P. Massa and C. Hayes, "Page-reRank: Using trusted links to re-rank author- ity," in Proceedings of the IEEE/WIC/ACM International Conference on WebIntelligence (WI), pp. 614–617, 2005.
[162] A. Mathes, "Filler Friday: Google Bombing," Retrieved June 1, 2009 from, April 2001.
[163] T. McNichol, "Engineering Google results to make a point," New York Times, January 2004.
[164] T. P. Metaxas and J. Destefano, "Web spam, propaganda and trust," in Proceedings of the First International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), May 2005.
[165] A. Metwally, D. Agrawal, and E. A. Abbadi, "Detectives: Detecting coalition hit inflation attacks in advertising networks streams," in Proceedings of the16th International Conference on World Wide Web (WWW), pp. 241–250,New York, NY, USA: ACM Press, 2007.
[166] A. G. Mishne, "Applied Text Analytics for Blogs," PhD thesis, University of Amsterdam, April 2007.
[167] G. Mishne, D. Carmel, and R. Lempel, "Blocking blog spam with language model disagreement," in Proceedings of the 1st International Workshop onAdversarial Information Retrieval on the Web (AIRWeb), May 2005.
[168] T. Moore and R. Clayton, "Evil searching: Compromise and recompromise of internet hosts for phishing," in Financial Cryptography and Data Security,pp. 256–272, Springer, 2009.
[169] M. Moran and B. Hunt, Search Engine Marketing, Inc. Upper Saddle River, NJ: IBM Press, 2006.
[170] A. Moshchuk, T. Bragin, D. S. Gribble, and M. H. Levy, "A crawler-based study of spyware on the web," in Proceedings of the Network and DistributedSystem Security Symposium (NDSS), pp. 17–33, February 2006.
[171] R. Moulton and K. Carattini, "A quick word about Googlebombs," Retrieved June 1, 2009 from, January 2007.
[172] L. Mui, M. Mohtashemi, and A. Halberstadt, "A computational model of trust and reputation," in Proceedings of the 35th Hawaii International Conferenceon System Science (HICSS), 2002.
[173] M. Najork, "System and method for identifying cloaked web servers," U.S.
Patent 6,910,077 (issued June 2005), 2002.
[174] N. Neubauer, R. Wetzker, and K. Obermayer, "Tag spam creates large non- giant connected components," in Proceedings of the 5th International Work-shop on Adversarial Information Retrieval on the Web (AIRWeb), pp. 49–52,ACM Press, 2009.
been Google bombed,",December 2003.
[176] L. Nie, D. B. Davison, and B. Wu, "Incorporating trust into Web author- ity," Technical Report LU-CSE-07-002, Department of Computer Science andEngineering, Lehigh University, 2007.
[177] L. Nie, B. Wu, and D. B. Davison, "A cautious surfer for PageRank," in Pro- ceedings of the 16th International Conference on World Wide Web (WWW),pp. 1119–1120, New York, NY, USA: ACM Press, 2007.
[178] L. Nie, B. Wu, and D. B. Davison, "Winnowing wheat from the chaff: Prop- agating trust to sift spam from the Web," in Proceedings of the 30th AnnualInternational ACM SIGIR Conference on Research and Development in Infor-mation Retrieval, pp. 869–870, July 2007.
[179] Y. Niu, Y.-M. Wang, H. Chen, M. Ma, and F. Hsu, "A quantitative study of forum spamming using context-based analysis," in Proceedings of the 14thAnnual Network and Distributed System Security Symposium (NDSS), pp. 79–92, February 2007.
[180] A. Ntoulas, "Crawling and searching the Hidden Web," PhD thesis, University of California at Los Angeles, Los Angeles, CA, USA, 2006.
[181] A. Ntoulas, J. Cho, and C. Olston, "What's new on the Web?: The evolu- tion of the Web from a search engine perspective," in Proceedings of the 13thInternational Conference on World Wide Web, pp. 1–12, New York, NY, USA:ACM Press, 2004.
[182] A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, "Detecting spam Web pages through content analysis," in Proceedings of the 15th International Con-ference on World Wide Web (WWW), pp. 83–92, May 2006.
[183] L. Page, S. Brin, R. Motwani, and T. Winograd, "The PageRank citation ranking: Bringing order to the Web," Techincal Report, Stanford University,1998. Available from
[184] R. C. Palmer, B. P. Gibbons, and C. Faloutsos, "ANF: A fast and scalable tool for data mining in massive graphs," in Proceedings of the Eighth ACMSIGKDD International Conference on Knowledge Discovery and Data Mining,pp. 81–90, New York, NY, USA: ACM Press, 2002.
[185] K. Park, V. S. Pai, K.-W. Lee, and S. Calo, "Securing Web service by auto- matic robot detection," in Proceedings of the USENIX Annual Technical Con-ference, pp. 255–260, 2006.
[186] J.-X. Parreira, D. Donato, C. Castillo, and G. Weikum, "Computing trusted authority scores in peer-to-peer networks," in Proceedings of the 3rd Interna-tional Workshop on Adversarial Information Retrieval on the Web (AIRWeb),pp. 73–80, ACM Press, May 2007.
[187] L. A. Penenberg, "Click fraud threatens Web," Wired, October 2004.
[188] L. A. Penenberg, "Legal showdown in search fracas," in Wired, Retrieved 09/68799, September 2005.
[189] A. Perkins, "The classification of search engine spam," Available online at [190] J. Piskorski, M. Sydow, and D. Weiss, "Exploring linguistic features for Web spam detection: A preliminary study," in Proceedings of the FourthInternational Workshop on Adversarial Information Retrieval on the Web(AIRWeb), pp. 25–28, New York, NY, USA: ACM, 2008.
[191] G. Price, "Google and Google bombing now included New Oxford Ameri- can Dictionary," Retrieved June 1, 2009 from http://blog.searchenginewatch.
com/blog/050516-184202, May 2005.
[192] N. Provos, P. Mavrommatis, A. M. Rajab, and F. Monrose, "All your iFRAMEs point to us," in Proceedings of the 17th USENIX Security Sym-posium, pp. 1–15, Berkeley, CA, USA: USENIX Association, 2008.
[193] N. Provos, D. McNamee, P. Mavrommatis, K. Wang, and N. Modadugu, "The ghost in the browser analysis of web-based malware," in Proceedings of theFirst Workshop on Hot Topics in Understanding Botnets (HotBots), 2007.
[194] X. Qi and D. B. Davison, "Knowing a web page by the company it keeps," in Proceedings of the 15th ACM International Conference on Information andKnowledge Management (CIKM), pp. 228–237, New York, NY: ACM Press,November 2006.
[195] X. Qi and D. B. Davison, "Web page classification: Features and algorithms," ACM Computing Surveys, vol. 41, no. 2, February 2009.
[196] X. Qi, L. Nie, and D. B. Davison, "Measuring similarity to detect qualified links," in Proceedings of the 3rd International Workshop on Adversarial Infor-mation Retrieval on the Web (AIRWeb), pp. 49–56, May 2007.
[197] R. J. Quinlan, C4.5: Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann, 1993.
[198] S. R. Rainwater, "Nigritude ultramarine FAQ," Retrieved June 1, 2009 from [199] Y. Rasolofo and J. Savoy, "Term proximity scoring for keyword-based retrieval systems," in ECIR, pp. 207–218, 2003.
[200] P. Resnick, K. Kuwabara, R. Zeckhauser, and E. Friedman, "Reputation sys- tems," Communications of the ACM, vol. 43, no. 12, pp. 45–48, 2000.
[201] M. Richardson, A. Prakash, and E. Brill, "Beyond PageRank: Machine learn- ing for static ranking," in Proceedings of the 15th International Conference onWorld Wide Web (WWW), pp. 707–715, New York, NY, USA: ACM Press,May 2006.
[202] G. Roberts and J. Rosenthal, "Downweighting tightly knit communities in World Wide Web rankings," Advances and Applications in Statistics (ADAS),vol. 3, pp. 199–216, 2003.
[203] S. Robertson, S. Walker, M. M. Beaulieu, M. Gatford, and A. Payne, "Okapi at TREC-4," in NIST Special Publication 500-236: The Fourth Text REtrievalConference (TREC-4), pp. 73–96, 1995.
[204] G. Salton, A. Wong, and S. C. Yang, "A vector space model for auto- matic indexing," Communications of the ACM, vol. 18, no. 11, pp. 613–620,November 1975.
[205] Y. Sato, T. Utsuro, Y. Murakami, T. Fukuhara, H. Nakagawa, Y. Kawada, and N. Kando, "Analysing features of Japanese splogs and characteristics of key-words," in Proceedings of the Fourth International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), pp. 33–40, New York, NY, USA:ACM, 2008.
[206] C. Schmidt, "Page hijack: The 302 exploit, redirects and Google," [207] F. Sebastiani, "Machine learning in automated text categorization," ACM Computing Surveys, vol. 34, no. 1, pp. 1–47, 2002.
[208] D. Sheldon, "Manipulation of PageRank and Collective Hidden Markov Mod- els," PhD thesis, Cornell University, 2009.
[209] G. Shen, B. Gao, T.-Y. Liu, G. Feng, S. Song, and H. Li, "Detecting link spam using temporal information," in Proceedings of the Sixth IEEE InternationalConference on Data Mining (ICDM), December 2006.
[210] N. Shrivastava, A. Majumder, and R. Rastogi, "Mining (social) network graphs to detect random link attacks," in Proceedings of the InternationalConference on Data Engineering (ICDE), IEEE CS Press, April 2008.
[211] F. Silvestri, "Mining query logs: Turning search usage data into knowledge," Foundations and Trends in Information Retrieval, vol. 3, 2009.
[212] A. Singhal, "Challenges in running a commercial search engine," Keynote presentation at SIGIR 2005, August 2005.
[213] M. Sirivianos, X. Yang, and K. Kim, "FaceTrust: Assessing the credibil- ity of online personas via social networks," Technical Report, Duke Univer-sity, 2009. Retrieved 15 June 2009 from˜msirivia/publications/facetrust-tech-report.pdf.
[214] M. Sobek, "PR0 — Google's PageRank 0 penalty," e-pr0.shtml, 2002.
[215] A. Stassopoulou and D. M. Dikaiakos, "Web robot detection: A probabilistic reasoning approach," Computer Networks, vol. 53, no. 3, pp. 265–278, Febru-ary 2009.
[216] A.-J. Su, C. Y. Hu, A. Kuzmanovic, and C.-K. Koh, "How to improve your Google ranking: Myths and reality," in Proceedings of the IEEE/WIC/ACMInternational Conference on Web Intelligence and Intelligent Agent Technol-ogy (WI-IAT), pp. 50–57, Vol. 1, IEEE, 2010.
[217] M. K. Svore, Q. Wu, C. J. C. Burges, and A. Raman, "Improving Web spam classification using rank-time features," in Proceedings of the Third Interna-tional Workshop on Adversarial Information Retrieval on the Web (AIRWeb),pp. 9–16, New York, NY, USA: ACM Press, 2007.
[218] P.-N. Tan and V. Kumar, "Discovery of Web robot sessions based on their navigational patterns," Data Mining and Knowledge Discovery, vol. 6, no. 1,pp. 9–35, 2002.
[219] E. Tardos and T. Wexler, "Network formation games and the potential func- tion method," in Algorithmic Game Theory, (N. Nisan, T. Roughgarden,E. Tardos, and V. Vazirani, eds.), Cambridge University Press, 2007.
[220] C. Tatum, "Deconstructing Google bombs: A breach of symbolic power or just a goofy prank?," First Monday, vol. 10, no. 10, October 2005.
[221] A. Thomason, "Blog spam: A review," in Proceedings of Conference on Email and Anti-Spam (CEAS), August 2007.
[222] A. Toffler, "The Third Wave," Bantam Books, 1980.
[223] N. Tran, B. Min, J. Li, and L. Submaranian, "Sybil-resilient online content voting," in Proceedings of the 6th Symposium on Networked System Designand Implementation (NSDI), 2009.
[224] T. Urvoy, E. Chauveau, P. Filoche, and T. Lavergne, "Tracking Web spam with HTML style similarities," ACM Transactions on the Web, vol. 2, no. 1,2008.
[225] T. Urvoy, T. Lavergne, and P. Filoche, "Tracking Web spam with hidden style similarity," in Proceedings of the Second International Workshop on Adversar-ial Information Retrieval on the Web (AIRWeb), August 2006.
[226] N. Vidyasaga, "India's secret army of online ad ‘clickers'," The Times of India, [227] L. A. von Ahn and L. Dabbish, "Labeling images with a computer game," in Proceedings of the Conference on Human Factors in Computing Systems(CHI), pp. 319–326, ACM Press, 2004.
[228] K. Walsh and G. E. Sirer, "Fighting peer-to-peer spam and decoys with object reputation," in Proceedings of the ACM SIGCOMM Workshop on Economicsof Peer-to-Peer systems (P2PECON), pp. 138–143, New York, NY, USA:ACM Press, 2005.
[229] W. Wang, G. Zeng, M. Sun, H. Gu, and Q. Zhang, "EviRank: An evi- dence based content trust model for Web spam detection," in Proceedingsof Workshop on Emerging Trends of Web Technologies and ApplicationsWAIM/APWeb, pp. 299–307, 2007.
[230] Y.-M. Wang, D. Beck, X. Jiang, and R. Roussev, "Automated web patrol with Strider HoneyMonkeys: Finding web sites that exploit browser vulnera-bilities," in Proceedings of the Network and Distributed System Security Sym-posium (NDSS), February 2006.
[231] Y.-M. Wang, M. Ma, Y. Niu, and H. Chen, "Spam double-funnel: Connecting Web spammers with advertisers," in Proceedings of the 16th InternationalConference on World Wide Web (WWW), pp. 291–300, New York, NY, USA:ACM Press, 2007.
[232] S. Webb, "Automatic Identification and Removal of Low Quality Online Infor- mation," PhD thesis, College of Computing, Georgia Institute of Technology,December 2008.
[233] S. Webb, J. Caverlee, and C. Pu, "Introducing the Webb spam corpus: Using email spam to identify Web spam automatically," in Proceedings of the ThirdConference on Email and Anti-Spam (CEAS), July 2006.
[234] S. Webb, J. Caverlee, and C. Pu, "Predicting Web spam with HTTP session information," in Proceeding of the 17th ACM Conference on Information andKnowledge Management (CIKM), pp. 339–348, New York, NY, USA: ACM,2008.
[235] H. I. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, 1999.
[236] B. Wu, "Finding and Fighting Search Engine Spam," PhD thesis, Department of Computer Science and Engineering, Lehigh University, March 2007.
[237] B. Wu and K. Chellapilla, "Extracting link spam using biased random walks from spam seed sets," in Proceedings of the 3rd International Workshop on Adversarial Information Retrieval on the Web (AIRWeb), pp. 37–44, NewYork, NY, USA: ACM Press, 2007.
[238] B. Wu and D. B. Davison, "Cloaking and redirection: A preliminary study," in Proceedings of the 1st International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), 2005.
[239] B. Wu and D. B. Davison, "Detecting semantic cloaking on the Web," in Proceedings of the 15th International World Wide Web Conference (WWW),pp. 819–828, ACM Press, 2006.
[240] B. Wu and D. B. Davison, "Undue influence: Eliminating the impact of link plagiarism on Web search rankings," in Proceedings of The 21st ACM Sym-posium on Applied Computing (SAC), pp. 1099–1104, April 2006.
[241] B. Wu and D. B. Davison, "Identifying link farm spam pages," in Spe- cial interest tracks and posters of the 14th International Conference onWorld Wide Web (WWW), pp. 820–829, New York, NY, USA: ACM Press,2005.
[242] B. Wu, V. Goel, and D. B. Davison, "Propagating trust and distrust to demote Web spam," in Workshop on Models of Trust for the Web (MTW), May 2006.
[243] B. Wu, V. Goel, and D. B. Davison, "Topical TrustRank: Using topicality to combat Web spam," in Proceedings of the 15th International World Wide WebConference (WWW), pp. 63–71, ACM Press, May 2006.
[244] L. Xiong and L. Liu, "PeerTrust: Supporting reputation-based trust for peer- to-peer electronic communities," IEEE Transactions on Knowledge and DataEngineering, vol. 16, no. 7, pp. 843–857, 2004.
[245] H. Yu, M. Kaminsky, B. P. Gibbons, and A. Flaxman, "SybilGuard: Defending against sybil attacks via social networks," in Proceedings of the ACM Con-ference on Applications, Technologies, Architectures, and Protocols for Com-puter Communications (SIGCOMM), pp. 267–278, New York, NY, USA: ACMPress, 2006.
[246] K. Yusuke, W. Atsumu, K. Takashi, B. B. Bahadur, and T. Toyoo, "On a referrer spam blocking scheme using Bayesian filter," Joho Shori Gakkai Shin-pojiumu Ronbunshu, vol. 1, no. 13, pp. 319–324, In Japanese, 2005.
[247] H. Zhang, A. Goel, R. Govindan, K. Mason, and B. V. Roy, "Making eigenvector-based reputation systems robust to collusion," in Proceedings ofthe Third Workshop on Web Graphs (WAW), pp. 92–104, Springer: Vol. 3243of Lecture Notes in Computer Science, October 2004.
[248] L. Zhang, Y. Zhang, Y. Zhang, and X. Li, "Exploring both content and link quality for anti-spamming," in Proceedings of the Sixth IEEE InternationalConference on Computer and Information Technology (CIT), IEEE ComputerSociety, 2006.
[249] Y. Zhang and A. Moffat, "Some observations on user search behavior," Aus- tralian Journal of Intelligent Information Processing Systems, vol. 9, no. 2,pp. 1–8, 2006.
[250] L. Zhao, Q. Jiang, and Y. Zhang, "From good to bad ones: Making spam detection easier," in Proceedings of the Eighth IEEE International Conferenceon Computer and Information Technology Workshops (CIT), IEEE ComputerSociety, 2008.
[251] B. Zhou, "Mining page farms and its application in link spam detection," Master's thesis, Simon Fraser University, 2007.
[252] B. Zhou and J. Pei, "Sketching landscapes of page farms," in Proceedings of the 7th SIAM International Conference on Data Mining (SDM), SIAM, April2007.
[253] B. Zhou, J. Pei, and Z. Tang, "A spamicity approach to Web spam detection," in Proceedings of the SIAM International Conference on Data Mining (SDM),April 2008.
[254] D. Zhou, C. J. C. Burges, and T. Tao, "Transductive link spam detection," in Proceedings of the 3rd International Workshop on Adversarial InformationRetrieval on the Web (AIRWeb), pp. 21–28, New York, NY, USA: ACM Press,2007.
[255] C.-N. Ziegler and G. Lausen, "Propagation models for trust and distrust in social networks," Information Systems Frontiers, vol. 7, no. 4–5, pp. 337–358,December 2005.
[256] R. P. Zimmermann, The Official PGP User's Guide. Cambridge, MA: MIT Press, 1995.
[257] J. Zittrain, The Future of the Internet — And How to Stop It. Yale University Press, April 2008.


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Meet the Need Teacher‘s Guide This project, "Meet the Need. Vocational Teaching Material Supporting the Integration of Migrants into the Labour Market" was funded by the European Commission under the Grundtvig Lifelong Learning Programme. In Austria, this project was also supported by the Austrian Federal Ministry for Education, Arts and Culture.