A framework to predict the quality of answers with non-textual features
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Finding high-quality content in social media
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Finding the right facts in the crowd: factoid question answering over social media
Proceedings of the 17th international conference on World Wide Web
A few bad votes too many?: towards robust ranking in social media
AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Learning to recognize reliable users and content in social media with coupled mutual reinforcement
Proceedings of the 18th international conference on World wide web
Understanding and summarizing answers in community-based question answering services
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Detecting comment spam through content analysis
WAIM'10 Proceedings of the 2010 international conference on Web-age information management
A comparative assessment of answer quality on four question answering sites
Journal of Information Science
Spotting fake reviewer groups in consumer reviews
Proceedings of the 21st international conference on World Wide Web
Serf and turf: crowdturfing for fun and profit
Proceedings of the 21st international conference on World Wide Web
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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 online detection of CQA spams.