Query-log mining for detecting spam

ABSTRACT

Every day millions of users search for information on the web via search engines, and provide implicit feedback to the results shown for their queries by clicking or not onto them. This feedback is encoded in the form of a query log that consists of a sequence of search actions, one per user query, each describing the following information: (i) terms composing a query, (ii) documents returned by the search engine, (iii) documents that have been clicked, (iv) the rank of those documents in the list of results, (v) date and time of the search action/click, (vi) an anonymous identifier for each session, and more.

In this work, we investigate the idea of characterizing the documents and the queries belonging to a given query log with the goal of improving algorithms for detecting spam, both at the document level and at the query level.

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Relevance feedback

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.4 Systems and Software

          Nouns: World Wide Web (WWW)

  I. Computing Methodologies
  I.5 PATTERN RECOGNITION
      I.5.4 Applications
          Subjects: Text processing

General Terms:
Design, Performance