A large-scale study of automated web search traffic

ABSTRACT

As web search providers seek to improve both relevance and response times, they are challenged by the ever-increasing tax of automated search query traffic. Third party systems interact with search engines for a variety of reasons, such as monitoring a website's rank, augmenting online games, or possibly to maliciously alter click-through rates. In this paper, we investigate automated traffic in the query stream of a large search engine provider. We define automated traffic as any search query not generated by a human in real time. We first provide examples of different categories of query logs generated by bots. We then develop many different features that distinguish between queries generated by people searching for information, and those generated by automated processes. We categorize these features into two classes, either an interpretation of the physical model of human interactions, or as behavioral patterns of automated interactions. We believe these features formulate a basis for a production-level query stream classifier.

REFERENCES

• 1: Eugene Agichtein , Eric Brill , Susan Dumais , Robert Ragno, Learning user interaction models for predicting web search result preferences, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148175]
• 2: Peter Anick, Using terminological feedback for web search refinement: a log-based study, Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval, July 28-August 01, 2003, Toronto, Canada [doi>10.1145/860435.860453]
• 3: Click Quality Team. How Fictitious Clicks Occur in Third-Party Click Fraud Audit Reports, Google, Inc, 2006.
• 4: Neil Daswani , Michael Stoppelman, The anatomy of Clickbot.A, Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets, p.11-11, April 10, 2007, Cambridge, MA
• 5: Dennis Fetterly , Mark Manasse , Marc Najork, Spam, damn spam, and statistics: using statistical analysis to locate spam web pages, Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004, June 17-18, 2004, Paris, France [doi>10.1145/1017074.1017077]
• 6: Thorsten Joachims, Optimizing search engines using clickthrough data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775067]
• 7: Thorsten Joachims , Laura Granka , Bing Pan , Helene Hembrooke , Geri Gay, Accurately interpreting clickthrough data as implicit feedback, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, August 15-19, 2005, Salvador, Brazil [doi>10.1145/1076034.1076063]
• 8: Maryam Kamvar , Shumeet Baluja, A large scale study of wireless search behavior: Google mobile search, Proceedings of the SIGCHI conference on Human Factors in computing systems, April 22-27, 2006, Montréal, Québec, Canada [doi>10.1145/1124772.1124877]
• 9: Anestis Karasaridis , Brian Rexroad , David Hoeflin, Wide-scale botnet detection and characterization, Proceedings of the first conference on First Workshop on Hot Topics in Understanding Botnets, p.7-7, April 10, 2007, Cambridge, MA
• 10: Travis Schluessler , Stephen Goglin , Erik Johnson, Is a bot at the controls?: Detecting input data attacks, Proceedings of the 6th ACM SIGCOMM workshop on Network and system support for games, p.1-6, September 19-20, 2007, Melbourne, Australia [doi>10.1145/1326257.1326258]
• 11: A. Tuzhilin. The Lane's Gifts v. Google Report.
• 12: Weka. http://www.cs.waikato.ac.nz/~ml/weka/
• 13: K.-L. Wu , P. S. Yu , A. Ballman, SpeedTracer: a Web usage mining and analysis tool, IBM Systems Journal, v.37 n.1, p.89-105, 1997

INDEX TERMS

Primary Classification:
  D. Software
  D.3 PROGRAMMING LANGUAGES
      D.3.3 Language Constructs and Features

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.m Miscellaneous

General Terms:
Algorithms, Experimentation, Measurement, Performance

Keywords:
bot, query, search