Determining Bias to Search Engines from Robots.txt

This piece forms part of our academic series of research pieces relating to search engine optimization. It is authored by Yang Sun, Ziming Zhuang, Isaac G. Councill, and C. Lee Giles (2007). All copyrights belong to the respective authors.

Abstract:

Search engines largely rely on robots (i.e., crawlers or spiders) to collect information from the Web. Such crawling activities can be regulated from the server side by deploying the Robots Exclusion Protocol in a file called robots.txt. Ethical robots will follow the rules specified in robots.txt. Websites can explicitly specify an access preference for each robot by name. Such biases may lead to a “rich get richer” situation, in which a few popular search engines ultimately dominate the Web because they have preferred access to resources that are inaccessible to others. This issue is seldom addressed, although the robots.txt convention has become a de facto standard for robot regulation and search engines have become an indispensable tool for information access.
We propose a metric to evaluate the degree of bias to which specific robots are subjected.

We have investigated 7,593 websites covering education, government, news, and business domains, and collected 2,925 distinct robots.txt files. Results of content and statistical analysis of the data confirm that the robots of popular search engines and information portals, such as Google, Yahoo, and MSN, are generally favored by most of the websites we have sampled. The results also show a strong correlation between the search engine market share and the bias toward particular search engine robots.

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