adversarial information retrieval on the web or how i spammed google and lost

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Adversarial Information Retrieval on the Web or How I spammed Google and lost Dr. Frank McCown Search Engine Development – COMP 475 Mar. 24, 2009

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Adversarial Information Retrieval on the Web or How I spammed Google and lost. Dr. Frank McCown Search Engine Development – COMP 475 Mar. 24 , 2009. Why are search engines and content providers adversaries?. Search engine’s primary goal: - PowerPoint PPT Presentation

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Page 1: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Adversarial Information Retrieval on the Web

orHow I spammed Google and lost

Dr. Frank McCownSearch Engine Development – COMP 475

Mar. 24, 2009

Page 2: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Page 3: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Why are search engines and content providers adversaries?

Incentives: $$$

Search engine’s primary goal:

Provide the most relevant results for the given query

Content provider’s primary goal:

Rank as high as possible in SERP for certain queries

Page 4: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Search engine optimization (SEO)

• White hat techniques– Follow published guidelines provided by search

enginesExcerpt from Google’s Webmaster Guidelines:

• Create a useful, information-rich site, and write pages that clearly and accurately describe your content.

• Make sure that your <title> elements and alt attributes are descriptive and accurate.

• Check for broken links and correct HTML.

http://www.google.com/support/webmasters/bin/answer.py?hl=en&answer=35769#1

http://www.google.com/support/webmasters/bin/answer.py?hl=en&answer=35769#1

Page 5: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Search engine optimization

• Black hat techniques– content spam (spamdexing)– comment spam, referrer spam– link-bombing (a.k.a. Google-bombing)– blog spam (splogs)– malicious tagging– reverse engineering of ranking algorithms

Page 6: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Assigning Relevance: TF-IDF

Which page is more relevant to the query “Harding football”?

Page 7: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Assigning Relevance: Link Analysis

PageRank: Links are a type of citation or recommendation. The more pages that point to you, the more important your page is, but links from more important pages receive higher PageRank.

Page 8: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Content Spam

http://www.mattcutts.com/blog/page/99/

Hidden text

http://www.mattcutts.com/blog/page/99/

Page 9: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Deliberate misspellings

Keyword stuffing

Gibberish text

http://www.mattcutts.com/blog/page/99/

http://www.mattcutts.com/blog/page/99/

Page 10: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Hidden link

http://www.mattcutts.com/blog/hidden-links/

http://www.mattcutts.com/blog/hidden-links/

Page 11: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Comment Spam

<a href="http://canadianpharm.com/" rel="nofollow">purchasing drugs online</a>

Page 12: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Cloaking

Web server

User agent: GooglebotGET: http://foo.com/

User agent: FirefoxGET: http://foo.com/

Page 13: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Spam Blogs (Splogs)

1http://www.adweek.com/aw/search/article_display.jsp?vnu_content_id=1001736416

In 2005, it was estimated that one in five blogs was spam.1

http://www.adweek.com/aw/search/article_display.jsp?vnu_content_id=1001736416

Page 14: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Google-bombing

• 2004: Google bomb contest for search term nigritude ultramarine

• 2004: Search for miserable failure shows whitehouse.gov as first result

• 2007: Google makes algorithmic changes to defuse most Google bombshttp://www.nytimes.com/2007/01/29/technology/29google.html?_r=1&oref=slogin

<a href=“http://microsoft.com/”>More evil than Satan himself</a>

Search engines use anchor text to help determine the relevance of a query.

http://www.nytimes.com/2007/01/29/technology/29google.html?_r=1&oref=slogin

Page 15: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Link Farms

Castillo et al., 2007, Know your neighbors: web spam detection using the web topology

Page 16: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Can we identify spam using statistical analysis?

Page 17: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Ntoulas et al., 2006, Detecting spam web pages through content analysis

Page 18: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Ntoulas et al., 2006, Detecting spam web pages through content analysis

Page 19: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Ntoulas et al., 2006, Detecting spam web pages through content analysis

Page 20: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Ntoulas et al., 2006, Detecting spam web pages through content analysis

Page 21: Adversarial Information Retrieval on the Web or How I spammed Google and lost

Combating Web Spam

• Statistical analysis of content• Statistical analysis of web topology• Trust measures like TrustRank• AIRWeb workshops

http://airweb.cse.lehigh.edu/ • Web Spam Challenge

http://webspam.lip6.fr/wiki/pmwiki.php

http://airweb.cse.lehigh.edu/

http://webspam.lip6.fr/wiki/pmwiki.php

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