Web Spam, Propaganda and Trust

Panagiotis T. Metaxas Joseph DeStefano Wellesley College College of the Holy Cross Wellesley, MA 02481, USA Worcester, MA 01610, USA [email protected] [email protected]

ABSTRACT Retrieval]: Miscellaneous Web spamming, the practice of introducing artificial text and links into web pages to affect the results of searches, has General Terms been recognized as a major problem for search engines. It is also a serious problem for users because they are not aware Algorithms, Experimentation, Social Networks, Propaganda, of it and they tend to confuse trusting the search engine with Trust trusting the results of a search [16]. The parallels between web spamming on the internet and propaganda in the real Keywords world suggest that we can use anti-propaganda techniques to educate users and develop tools to help them evaluate the search, Web graph, link structure, PageRank, HITS, Web reliability of the information they find online. spam In this paper, we first analyze the effects that web spam has on the evolution of the search engines and their relation- 1. INTRODUCTION ship to propagandistic techniques in society. Then, we ex- The web has changed the way we inform and get informed. amine the neighborhoods of untrustworthy sites, finding that Every organization has a web site and people are increas- a dense biconnected component (BCCs) containing the site ingly comfortable accessing it for information for any ques- provide a reasonable trust neighborhood that has parallels in tion they may have. The exploding size of the web necessi- social network theory. The fact that spammers employ pro- tated the development of search engines and web directories. pagandistic techniques enables us to design a heuristic that Most people with online access use a search engine to get in- follows anti-propagandistic practices in order to recognize a formed and make decisions that may have medical, financial, spamming network. In society, recognition of an untrustwor- cultural, political, security or other important implications thy message (in the opinion of a particular person or other [10, 37, 23, 29]. Moreover, 85% of the time, people do not social entity) is a reason for questioning the entities that rec- look past the first ten results returned by the search en- ommend the message. Entities that are found to strongly gine [35]. Given this, it is not surprising that anyone with support more untrustworthy messages become untrustwor- a web presence struggles for a place in the top ten posi- thy themselves. So, social distrust is propagated backwards tions of relevant web search results. The importance of the for a number of steps. Our heuristic simulates this behavior top-10 placement has given birth to a new industry, which on the trust neighborhood of a spammer. claims to sell know-how for prominent placement in search In our experiments, we examined trust neighborhoods of results and includes companies, publications, and even con- web sites, both trustworthy and not. Our findings suggest ferences. Some of them are willing to bend the truth in order that spamming networks can be reliably recognized from to fool the search engines and their customers, by creating their relationship to a single untrustworthy starting point by web pages containing web spam. backward propagation of distrust. Further, nodes involved Web spamming is the practice of manipulating web pages in a spamming network can be divided into two groups: in order to cause search engines to rank some web pages those that have content similar to the starting site (aka “link higher than they would without any manipulation.1 The farms”), and those that have dissimilar content (aka “mu- motive is usually commercial, but can also be political, or tual admiration societies”). Our tool explores thousands of religious. nodes within minutes and could be deployed at the browser- The creators of web spam are often specialized companies level, making it possible to resolve the moral question of who selling their expertise as a service, but can also be the web should be making the decision of weeding out spammers in masters of the companies and organizations that would be favor of the end user. their customers. Spammers attack search engines through text and link manipulations [22, 18]: Categories and Subject Descriptors 1We should mention here that there is not a complete agree- H.3.3 [Information Storage and Retrieval]: Information ment on the definition of web spam among authors, which Search and Retrieval; H.3.m [Information Storage and leads to some confusion. Moreover, to people unfamiliar Copyright is held by the author/owner(s). with web spam, the term is mistaken for email spam. A WWW2005, May 10–14, 2005, Chiba, Japan. more descriptive name for it would be “search engine rank- . ing manipulation” or “adversarial information retrieval”. • Text spam: This includes excessively repeating text indicate that at least 8% of all pages indexed is spam [12] and/or adding irrelevant text on the page that will while experts consider web spamming the single most diffi- cause incorrect calculation of page relevance; adding cult challenge web searching is facing today [22]. misleading meta-keywords or irrelevant “anchor text” In this paper we first examine the reasons web spamming that will cause incorrect application of rank heuristics. has been so successful and its relationship to social propa- ganda. Then, we develop heuristics that are able to recog- • Link spam: This technique aims to change the per- nize web neighborhoods, especially untrustworthy ones. We ceived structure of the webgraph in order to cause in- present experimental results that show considerable success correct calculation of page reputation. Such examples in recognizing spamming neighborhoods. Finally, we dis- are the so-called “link-farms”, “mutual admiration so- cuss what we believe should be a frame for the long-term cieties”, page “awards”, domain flooding (plethora of approach to web spam. domains that re-direct to a target site), etc.

Both kinds of spam aim to boost the ranking of spammed 2. THE WEBGRAPH AS A SOCIAL NET- web pages. Sometimes cloaking is included as a third spam- WORK ming technique [22, 19]. Cloaking aims to serve different The web is typically represented by a directed graph [8]. pages to search engine robots and to web browsers (users). The nodes in the webgraph are the pages (or sites) that These pages could be created statically or dynamically. Static reside on servers on the internet. Arcs correspond to hy- pages, for example, may employ hidden links and/or hidden perlinks that appear on web pages (or sites). The theory text with colors or small font sizes noticeable by a crawler of social networks [38] also uses directed graphs to repre- but not by a human. Dynamic pages might change content sent relationships between social entities. The nodes (called on the fly depending on the visitor, fake the clickstream or “actors”) correspond to social entities (e.g., people, institu- query stream, submit millions of pages to “add-URL” forms tions, ideas). Arcs (called “ties”) correspond to social rela- of search engines, etc. We consider the false links and text tions between the entities they connect (e.g., has influence themselves to be the spam, while, strictly speaking, cloaking on, knows, trusts). is a tool that helps spammers hide their attacks. This connection is more than a similarity in descriptions. Since anyone can be an author on the web, these prac- The web itself is a social creation, and both PageRank and tices have naturally created a question of information reli- HITS are socially inspired algorithms [6, 26]. Socially in- ability. An audience used to trusting the written word of spired systems are subject to socially inspired attacks, how- newspapers and books is unable, unprepared or unwilling ever. Not surprisingly then, the theory of propaganda [28] to think critically about the information obtained from the can provide intuition into the dynamics of the web. web. In a recent study [16] we found that while college For example, PageRank is based on the assumption that students regard the web as a primary source of informa- the reputation of an entity (a web site in this case) can be tion, many do not check more than a single source, and measured as a function of both the number and reputation have trouble recognizing trustworthy sources online. In par- of other entities recommending it. A link to a web page ticular, two out of three students are consistently unable is counted as a “vote of confidence” to this web site, and to differentiate between facts and advertising claims, even in turn, the reputation of a page is divided among those “infomercials.” At the same time, they have considerable it is recommending [6]. Since HTML does not provide for confidence in their abilities to distinguish trustworthy sites “positive” and “negative” links, all links are taken as posi- from non-trustworthy ones, especially when they feel tech- tive. This is not always true, but is considered a reasonable nically competent. We have no reason to believe that the assumption. general public will perform any better than well-educated More importantly, there is also the implicit assumption is students. In fact, a recent analysis of internet related fraud that hyperlink “voting” is taking place independently, with- by a major Wall Street law firm [10] put the blame squarely out prior agreement or central control. Spammers, like social on the investors for the success of stock fraud cases. propagandists, are groups of sites that are able to gather a One of the reasons behind the users’ difficulty to distin- large number of such “votes of confidence” by design, thus guish trustworthy from untrustworthy information comes breaking the assumption of independence in a hyperlink. from the success that both search engines and spammers Search engines consider such moves spam, and would like to have enjoyed in the last decade. Users have come to trust restrict it, but there can be no algorithm that can recognize search engines as a means of finding information, and spam- spamming sites automatically based on graph isomorphism mers have successfully managed to get them to transfer that [5]. trust to the results of the search. There is clearly a need for education of the users, so that people develop a healthy sus- picion of unverified search results. Beyond that, though, 3. EVOLUTION OF THE SEARCH ENGINES there is a need for browser-level tools that will help the user In the early 90’s, when the web numbered just a few mil- move from suspicion to decision in determining which sites lion servers, the first generation search engines were rank- to trust. ing search results using classic information retrieval tech- From their side, the search engines have struggled to de- niques: the more rare words two documents share, the more liver spam-free results and have developed sophisticated search similar they are considered to be [34, 21]. A search query Q result ranking strategies. Two such ranking strategies that is simply a short document and the results of a search for Q have received major attention are the PageRank [6, 2] and are ranked according to their (normalized) similarity to the HITS [26] algorithms. Achieving high PageRank has become query which was treated as the value of the page. a sort of obsession for many companies’ IT departments, and The first attack to this “tf.idf ranking,” as it is known, the raison d’ˆetre of spamming companies. Some estimates came from within the search engines. Around 1995, search engines started selling search keywords to advertisers as a ways of circumventing PageRank. In PageRank, a page en- way of generating revenue: If a search query contained a joys some “absolute reputation”, that is, its reputation is not “sold” keyword, the results would include targeted adver- restricted on some particular issue. So, spammers develop tisement and a higher ranking for the link to the sponsor’s sites with expertise on irrelevant subjects, and they justifi- web site. This is the first time we have a socially inspired ably acquire high ranking on their expert sites. Then they ranking, which follows marketing practices of the real world. interlink their networked sites with the expert sites, creat- Mixing search results with paid advertisement raised se- ing what is called a “mutual admiration society” (MAS), rious ethical questions, but also showed the way to financial causing all sites to share a higher PageRank and the search profits to spammers who started their own attacks by creat- engine is fooled. This is the well-known propagandistic tech- ing pages containing many rare keywords to obtain a higher nique known as testimonials, where well known people (en- ranking score. In terms of propaganda theory, the spammers tertainers, public figures, etc.) offer their opinion on issues employed a variation of the technique of glittering general- about which they are not experts [28, 74]. HITS has also ities to confuse the first generation search engines [28, 47]. shown to be highly spammable by this technique [25] due The propagandist associates one or more suggestive words to the fact that its effectiveness depends on the accuracy of without evidence to alter the conceived value of a person or the initial neighborhood calculation. idea. To avoid spammers (and public embarrassment from The table below summarizes our findings for the first three the keyword selling practice), search engines would keep se- generation of search engines and the correspondence be- cret their exact ranking algorithm. Secrecy is no defense, tween web spam and social propaganda. however, since secret rules can be figured out by experimen- tation and reverse engineering (e.g., [33, 30]). SE Ranking Spamming Propaganda Second generation search engines started employing 1st Gen Doc keyword glittering more sophisticated ranking techniques in an effort to nul- Similarity stuffing generalities lify the effects of glittering generalities. One of the more 2nd Gen + Site + link + bandwagon successful ones was based on the “link voting principle”: popularity farms Each web site s has value equal to its “popularity”, which 3rd Gen + Page + mutual + testimonials is influenced by the set B of sites pointing to site s. Lycos s reputation admiration soc. became the champion of this ranking technique and had its own popularity skyrocket around 1996 [31]. Doing so, it was also distancing itself from the ethical questions introduced Web search corporations are reportedly busy developing by combining advertising with ranking. the engines of the next generation [7]. The new search en- Unfortunately, this ranking method did not succeed in gines hope to be able to recognize the need behind the query stopping spammers either. Spammers started creating clus- of the user. Given the success the spammers have enjoyed ters of interconnected web sites that had identical or similar so far, one wonders how will they spam the fourth genera- contents with the site they were promoting, which subse- tion engines. Is it possible to create a ranking that is not quently became known as “link farms” (LF). The link voting spammable? Put another way, can the web as a social space principle was socially inspired, so spammers used the well be free of propaganda? Seen in this light, it appears that we known propagandistic method of bandwagon to circumvent are trying to create in cyberspace what human societies have it [28, 105]. Using this technique, the propagandist is pro- not succeeded in creating in their social space. However, as moting the impression of a high degree of recommendation in society, we can learn to live successfully with propaganda, by inter-linking many internally controlled sites that will given appropriate education and technology. eventually all share high ranking. The introduction of PageRank in 1998 was a major de- 4. EXPLORING WEB NEIGHBORHOODS velopment for search engines, because it seemed to provide Since spammers employ propagandistic techniques, as we a more sophisticated anti-spamming solution to the band- have argued above, it makes sense to design anti-propagandistic wagon technique. Under PageRank, not every link con- methods for defending against them. These methods need tributes equally to the reputation of a page. Instead, links to be user-guided. Propaganda, after all, [11], does not al- from highly reputable pages contribute much higher than ways have a negative connotation. Advertisement is a form links from other sites. That way, the site networks devel- of propaganda that we have all learned to live with. The oped by spammers would not influence much their PageR- “art of persuasion” is objectionable mainly when it is used ank, and Google became the search engine of choice. A to promote an untrustworthy message according to the re- page p has value equal to its reputation R(p) which is cal- ceiver’s opinion. When such an untrustworthy message is culated as the sum of fractions of the reputations of the set detected, it becomes a reason for us to reconsider the mes- Bp of pages pointing to p. HITS is another socially-inspired senger. Messengers who strongly support an untrustworthy ranking which has also received a lot of attention [26]. The message become untrustworthy themselves. This process is HITS algorithm divides the sites related to a query between selectively repeated for a few steps, propagating the distrust “hubs” and “authorities”. Hubs are sites that contain many of the original back to those who show support for it. The links to authorities, while authorities are sites pointed to by results of this process become part of the user’s belief system the hubs. (This circular definition can be resolved.) and are used to filter future information. PageRank and HITS marked the development of the third Propagation of distrust contrasts with the propagation generation.2 Unfortunately, spammers have again found of trust in that it progresses backwards through the graph,

2 i.e. a page’s reliability decreases if it links to untrustworthy [7] considers the search engines in our 2nd and 3rd gener- sites. Current algorithms for ranking pages propagate trust ation to be in the same group. We believe that both the ranking and attack methods puts them in different cate- gories. forwards, i.e., a page’s rank is increased if a trustworthy site links to it. The heuristic in this paper focuses solely

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The subgraph that heavily sup- ports the starting site becomes suspicious for spam- according to the evaluator’s opinion, was clearly untrustwor- ming. In our experiments we found that the bicon- thy. In particular, the evaluator decided that he does not nected component (BCC) of the graph that includes s believe that a product for $39.95 would significantly increase is such an appropriate subgraph. muscle mass without increased exercise, decrease fat with- out change in diet or habits, enhance sexual performance, We divide the sites in the BCC into two groups: Those increase the good cholesterol while decreasing the bad, re- that have contents similar to s, and those that do not. grow hair, decrease blood pressure, remove wrinkles, and The former are considered members of a link farm, increase memory retention. In our experiments, we exam- while the latter are considered members of a mutual ined the neighborhoods six such sites, as well as two sites admiration society. The members of the link farm are judged to be trustworthy, labeled below as U-1 to U-6 and discredited, and the members of the MAS are down- T-1 to T-2, respectively. graded (although this result could perhaps be user- Figure 1 is a picture of a successful spammer U-1. The selectable, for a less conservative filtering). experiment revealed a neighborhood of 1380 sites, 266 of which were connected with 593 edges into a BCC forming 5. EXPERIMENTAL RESULTS the 3-trust neighborhood of the target site. The density We define the h-trust neighborhood of some site S as the and the size of the BCC is comparable to some of the larger largest biconnected component containing S of the graph BCCs of sites that openly promote “reciprocal linking”. composed of the sites that are no more than h links away The algorithm we outlined in the previous section would from S. In our experiments, we examined 3-trust neigh- be very difficult to implement completely and efficiently at borhoods of web sites, both trustworthy and not. We al- the browser side. We convert it to a heuristic that can be ready know that the neighborhoods of spammers and non- implemented on an average workstation and produce results spammers cannot be distinguished simply on graph-theoretic within minutes. The goal of implementing a quick algorithm terms [14, 5]. They can, however, be recognized from an introduces a number of simplifications explained below. untrustworthy starting point by backward propagation of To gather the back links of a site we use the Google API distrust. [15]. Still, some sites can have thousands of back links while To evaluate the trustworthiness of each site we had an others have only a few. For this experiment, we limited evaluator look at the sites of the BCC. A site was then de- the number of backlinks for each site to 30. We call this termined to be either Trustworthy, Untrustworthy, or Non- parameter the backlink fan. determined. The last category includes a variety of sites for Determining the similarity of a site to the starting site was which the evaluator could not make a clear determination done by sampling a few pages of each site. We have noticed due to the language used in the site, the subject matter, or from other experiments that when one samples 5–10 pages the fact that a Blog or Directory can not fall simply into per site one can get an excellent similarity measure. To one of the U/T categories. speed things up in this experiment, however, we only sam- It is perhaps valuable to reiterate here the fact that we are pled two pages per site. Even such a small sample, though, considering trustworthiness to be a personal decision, not an produced more than acceptable results. absolute quality of a site. One person’s gospel is another’s Similarity was determined using the df.idf ranking on the political propaganda, and our goal is to find tools that help universe of the sites explored. To decide on the cutoff point individuals make more informed decisions about the quality between LF’s and MAS’s, we created a site that contained a of the information they find on the web. few random news pages from Reuters. We call this site the In selecting the theme to evaluate, we considered a num- divider site. Pages more similar to the target site than is the ber of commercial, political, medical and financial issues. divider site are categorized as LF sites; others are grouped For the purpose of this paper we focused on a subject that, into MAS sites. Finally, to further reduce the effect of the explosive nature and ways to maximize it in a spamming network is described of the web, we introduced the concept of stop sites. A stop in [5]. A modification to the PageRank to take into account site is one that the user believes should not be included in the opinions of human editors, employees of a search engine, trust graph either because the trustworthiness of such a site is presented in [19]. A comprehensive treatment on social is known or because it cannot be defined. In the first group networks is presented in [38]. The connection between web we placed educational institutions as determined by their spammers and social propagandists, and how the evolution URL. In the latter we placed a few well known Directories of search engines can be understood as response to spam- and Blog sites. mers is presented in [32]. Propagation methods for trust We recognize that each of the decisions above can be and distrust are discussed in [17]. Some work on person- strengthened, thus strengthening the results of our approach. alized web search is presented in [20, 25]. The effect that In particular, increasing the fan parameter above 30 will search engines have on page popularity was discussed in [9]. recognize more sites in the neighborhood. Using a larger universe in calculating similarity and increasing the sam- pling of pages per site, will give a better approximation of 7. CONCLUSIONS AND FUTURE DIREC- the LF and MAS groups. Our rather conservative approach TIONS provided a solid proof of concept for our hypothesis, while In this paper we have argued that web spam is to cyber- remaining fast enough for browser-side implementation. world what propaganda is to society. As far as we know, We ran a breadth-first search on the backlinks for each this is the first time this relationship is noted. As evidence target site looking at the 3-trust neighborhood with a fan of this analogy and its importance, we have shown that the of 30 sites. We categorized the sites in the neighborhood evolution of search engines can be simply understood as the into members of a link farm (LF) or members of a mutual search engines’ response to defend against spam. New search admiration society (MAS) based on their similarity to the engines are not invented every few years, as it is sometimes target site. The sites were then evaluated for trustworthi- reported; they are developed when researchers have a good ness. Due to the effort involved, only a randomly chosen answer to spam. More importantly, we have shown that this 10% of the MAS sites were evaluated. All of the LF sites relationship can guide us towards developing heuristics that were evaluated, however. recognize spammers. In particular, we have presented auto- As you can see from the results below, there were almost matic ways of recognizing trust neighborhoods on the web no trustworthy sites in the 3-trust neighborhoods of the un- based on the biconnected component around some starting trustworthy ones. As one might expect, a trustworthy site site. Experimental results from a number of such instances is unlikely to deliberately link to an untrustworthy one, or show our algorithm’s ability of recognizing parts of a spam- even to one that “associates” with one. Not surprisingly, the ming network. statement is not as strong for the trustworthy sites, since un- With such results, the question arises as to what one trustworthy sites are free to link to whomever they choose should do once one recognizes a spamming network. This (although thanks to PageRank, spammers are unlikely to is a question that has not attracted much attention in the want to link to too many sites outside their spamming net- past. The “obvious” approach is that a search engine would work in order to avoid “leaking” rank [5]). delete such networks from its indices [12] or might down- grade them by some prespecified amount [19] as it has been Target T(LF) T(MAS) U(LF) U(MAS) reported in the past [36]. U-1 0% 0% 96% 88% Both of these approaches, however, require a universal U-2 0% 0% 100% 65% agreement of what constitutes spam. Such an agreement U-3 0% 0% 95% 100% cannot exist; one person’s spam may be another person’s U-4 0% 0% 88% 83% treasure. Should the search engines determine what is trust- U-5 0% 0% 100% 73% worthy and what is not? Willing or not, they are the de facto U-6 11% 9% 89% 57% arbiters of what information users see. As in a well-known T-1 86% 70% 14% 0% cartoon, the kid responds to the old man who has been look- T-2 64% 64% 33% 13% ing all his life for the meaning of life: “If it is not on Google or eBay, it does not exist.” Our experiments showed that the quality of the starting We believe that it is the users’ right and responsibility site was a very good predictor for the quality of the BCC to decide what is acceptable for them. Their browser, their sites. While most of the results above show no accidental or window to cyberworld, should enhance their ability to make erroneous linking of a trustworthy site to an untrustworthy this decision. User education is fundamental: People should one, we found such evidence in one experiment. know how search engines work and why, and how infor- mation appears on the web. But they should also have a browser that can help them determine the validity and 6. RELATED WORK trustworthiness of information. Web spamming has received a lot of attention lately [1, 3, The tool we described in an earlier section is a first step 4, 5, 12, 13, 19, 21, 22, 24, 27, 29, 30, 33]. The first papers in this direction. Ultimately, it would be used along with a to raise the issue were [30, 22]. The spammers’ success was set of trust certificates that contains the portable trust pref- noted in [13, 12, 10, 2, 4, 16, 23]. erences of the user, a set of preferences that the user can Characteristics of spamming sites based on diversion from accumulate over time. A combination of search engines ca- power laws are presented in [12]. Current tricks employed pable of providing indexed content and structure, including by spammers are detailed in [18]. 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