›› 2015, Vol. 30 ›› Issue (4): 810-828.doi: 10.1007/s11390-015-1562-x

Special Issue: Artificial Intelligence and Pattern Recognition; Data Management and Data Mining

• Special Section on Data Management and Data Mining • Previous Articles     Next Articles

The Best Answers? Think Twice: Identifying Commercial Campagins in the CQA Forums

Cheng Chen(陈诚), Student Member, ACM, IEEE, Kui Wu(吴逵), Senior Member, IEEE, Member, ACM, Venkatesh Srinivasan, Kesav Bharadwaj R   

  1. Department of Computer Science, University of Victoria, Victoria, V8P 5C2, Canada
  • Received:2015-01-26 Revised:2015-03-17 Online:2015-07-05 Published:2015-07-05
  • About author:Cheng Chen received his B.S. degree in computer science from Beijing University of Posts and Telecommunications, Beijing, in 2010. He received his M.S. degree in computer science from the University of Victoria, Canada, in 2012, and he is currently a Ph.D. candidate there. His research interests are online social networks, recommender systems, and distributed algorithms for graph mining.
  • Supported by:

    This research was partially supported by the Natural Sciences and Engineering Research Council of Canada under Grant No. 195819339 and the Globalink Internship of Mathematics of Information Technology and Complex Systems (MITACS) of Canada.

In an emerging trend, more and more Internet users search for information from Community Question and Answer (CQA) websites, as interactive communication in such websites provides users with a rare feeling of trust. More often than not, end users look for instant help when they browse the CQA websites for the best answers. Hence, it is imperative that they should be warned of any potential commercial campaigns hidden behind the answers. Existing research focuses more on the quality of answers and does not meet the above need. Textual similarities between questions and answers are widely used in previous research. However, this feature will no longer be effective when facing commercial paid posters. More context information, such as writing templates and a user's reputation track need to be combined together to form a new model to detect the potential campaign answers. In this paper, we develop a system that automatically analyzes the hidden patterns of commercial spam and raises alarms instantaneously to end users whenever a potential commercial campaign is detected. Our detection method integrates semantic analysis and posters' track records and utilizes the special features of CQA websites largely different from those in other types of forums such as microblogs or news reports. Our system is adaptive and accommodates new evidence uncovered by the detection algorithms over time. Validated with real-world trace data from a popular Chinese CQA website over a period of three months, our system shows great potential towards adaptive detection of CQA spams.

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