1. Know your Neighbors: Web Spam Detection Using the Web Topology Presented By, SOUMO GORAI Carlos Castillo(1), Debora Donato(1), Aristides Gionis(1), Vanessa Murdock(1), Fabrizio Silvestri(2). 1. Yahoo! Research Barcelona – Catalunya, Spain 2. ISTI-CNR –Pisa,Italy ACM SIGIR, 25 July 2007, Amsterdam

2. Soumo’s Biography 4 th Year CS Major Graduating May 2008 Interesting About Me: Lived in India, Australia, and the U.S. CS Interests: Databases, HCI, Web Programming, Networking, Graphics, Gaming,.

3. Here’s all that you can find on the web….

4. Here’s just some of what really is out there…

5. And more….

6. Why so many different things…? There is a fierce competition for your attention! Ease of publication for personal publication as well as commercial publication, advertisements, and economic activity. …and there’s lots lots lots lots…lots of spam!

7. What’s Spam?!

8. Hidden Text

9. Only hidden text? Here’s a whole fake search engine!!!

10. Why is Spam bad? Costs: Costs for users: lower precision for some queries Costs for search engines: wasted storage space, network resources, and processing cycles Costs for the publishers: resources invested in cheating and not in improving their contents Every undeserved gain in ranking for a spammer is a loss of search precision for the search engine.

11. How Do We Detect Spam? Machine Learning/Training Link-based Detection Content-based Detection Using Links and Contents Using Web-based Topology

12. Machine Learning/Training

13. ML Challenges Machine Learning Challenges: Instances are not really independent (graph) Training set is relatively small Information Retrieval Challenges: It is hard to find out which features are relevant It is hard for search engines to provide labeled data Even if they do, it will not reflect a consensus on what is Web Spam

14. Link-based Detection Single-level farms can be detected by searching groups of nodes sharing their out-links [Gibson et al., 2005]

15. Why use it? Degree-related measures PageRank TrustRank [Gy¨ongyi et al., 2004] Truncated PageRank [Becchetti et al., 2006]: similar to PageRank, it limitsa page to the PageRank score of its close neighbors. Thus, the Truncated PageRank score is a useful feature for spam detection because spam pages generally try to reinforce their PageRank scores by linking to each other.”

16. Degree-based Measures are related to in- degree and out-degree Edge-reciprocity (the number of links that are reciprocal) Assortativity (the ratio between the degree of a particular page and the average degree of its neighbors

17. TrustRank / PageRank TrustRank: an algorithm that picks trusted nodes derived from page-ranks but tests the degree of relationship one page has with other known trusted pages. This is given a TrustRank score. Ratio between TrustRank and Page Rank Number of home pages. Cons: this alone is not sufficient as there are many false positives.

18. Content-based Detection Most of the features reported in [Ntoulas et al., 2006] Number of words in the page and title Average word length Fraction of anchor text Fraction of visible text Compression rate Corpus precision and corpus recall Query precision and query recall Independent trigram likelihood Entropy of trigrams

19. Corpus and Query F: set of most frequent terms in the collection Q: set of most frequent terms in a query log P: set of terms in a page Computation Techniques: corpus precision: the fraction of words(except stopwords) in a page that appear in the set of popular terms of a data collection. corpus recall: the fraction of popular terms of the data collection that appear in the page. query precision: the fraction of words in a page that appear in the set of q most popular terms appearing in a query log. query recall: the fraction of q most popular terms of the query log that appear in the page.

20. Visual Clues Figure: Histogram of the average word length in non-spam vs. spam pages for k = 500. Figure: Histogram of the corpus precision in non-spam vs. spam pages. Figure: Histogram of the query precision in non-spam vs. spam pages for k = 500.

21. Links AND Contents Detection Why Both?:

22. Web Topology Detection Pages topologically close to each other are more likely to have the same label (spam/nonspam) than random pairs of pages. Pages linked together are more likely to be on the same topic than random pairs of pages [Davison, 2000] Spam tends to be clustered on the Web (black on figure)

23. Topological dependencies: in- links Let S OUT (x) be the fraction of spam hosts linked by host x out of all labeled hosts linked by host x. This figure shows the histogram of S OUT for spam and non-spam hosts. We see that almost all non-spam hosts link mostly to non-spam hosts. Let S IN (x) be the fraction of spam hosts that link to host x out of all labeled hosts that link to x. This figure shows the histograms of S IN for spam and non-spam hosts.In this case there is a clear separation between spam and non-spam hosts.

24. Clustering: if the majority of a cluster is predicted to be spam then we change the prediction for all hosts in the cluster to spam. The inverse holds true too.

25. Article Critique Pros: Has detailed descriptions of various detection mechanisms. Integrates link and content attributes for building a system to detect Web spam Cons: Statistics and success rate for other content-based detection techniques. Some graphs had axis labels missing. Extension: combine the regularization (any method of preventing overfitting of data by a model) methods at hand in order to improve the overall accuracy

26. Summary Machine Learning/Training Link-based Detection Content-based Detection Using Links and Contents Using Web-based Topology Costs: Costs for users: lower precision for some queries Costs for search engines: wasted storage space, network resources, and processing cycles Costs for the publishers: resources invested in cheating and not in improving their contents Every undeserved gain in ranking for a spammer, is a loss of precision for the search engine. How Do We Detect Spam?Why is Spam bad?