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

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• 作者：Cheng Chen ; Kui Wu ; Venkatesh Srinivasan…
• 关键词：CQA forum ; anomaly detection ; paid poster ; online detection system
• 刊名：Journal of Computer Science and Technology
• 出版年：2015
• 出版时间：July 2015
• 年：2015
• 卷：30
• 期：4
• 页码：810-828
• 全文大小：1,114 KB
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• 作者单位：Cheng Chen (1)
  Kui Wu (1)
  Venkatesh Srinivasan (1)
  R. Kesav Bharadwaj (1)
  
  1. Department of Computer Science, University of Victoria, Victoria, V8P 5C2, Canada
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Software Engineering
  Theory of Computation
  Data Structures, Cryptology and Information Theory
  Artificial Intelligence and Robotics
  Information Systems Applications and The Internet
  Chinese Library of Science
  
• 出版者：Springer Boston
• ISSN：1860-4749

文摘

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, needs 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.