Review spam detection

ABSTRACT

It is now a common practice for e-commerce Web sites to enable their customers to write reviews of products that they have purchased. Such reviews provide valuable sources of information on these products. They are used by potential customers to find opinions of existing users before deciding to purchase a product. They are also used by product manufacturers to identify problems of their products and to find competitive intelligence information about their competitors. Unfortunately, this importance of reviews also gives good incentive for spam, which contains false positive or malicious negative opinions. In this paper, we make an attempt to study review spam and spam detection. To the best of our knowledge, there is still no reported study on this problem.

REFERENCES

• 1: A. Broder, On the Resemblance and Containment of Documents, Proceedings of the Compression and Complexity of Sequences 1997, p.21, June 11-13, 1997
• 2: Kushal Dave , Steve Lawrence , David M. Pennock, Mining the peanut gallery: opinion extraction and semantic classification of product reviews, Proceedings of the 12th international conference on World Wide Web, May 20-24, 2003, Budapest, Hungary [doi>10.1145/775152.775226]
• 3: Gyongyi, Z., & Garcia-Molina, H. Web Spam Taxonomy. Technical Report, Stanford University, 2004.
• 4: Minqing Hu , Bing Liu, Mining and summarizing customer reviews, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014073]
• 5: Nitin Jindal , Bing Liu, Identifying comparative sentences in text documents, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148215]
• 6: Jindal, N. & Liu, B. Review Analysis. Tech. Report, 2007.
• 7: Kang Li , Zhenyu Zhong, Fast statistical spam filter by approximate classifications, Proceedings of the joint international conference on Measurement and modeling of computer systems, June 26-30, 2006, Saint Malo, France
• 8: Liu, B. Web Data Mining. Springer, 2007.
• 9: Alexandros Ntoulas , Marc Najork , Mark Manasse , Dennis Fetterly, Detecting spam web pages through content analysis, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135794]
• 10: Ana-Maria Popescu , Oren Etzioni, Extracting product features and opinions from reviews, Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing, p.339-346, October 06-08, 2005, Vancouver, British Columbia, Canada [doi>10.3115/1220575.1220618]
• 11: Baoning Wu , Vinay Goel , Brian D. Davison, Topical TrustRank: using topicality to combat web spam, Proceedings of the 15th international conference on World Wide Web, May 23-26, 2006, Edinburgh, Scotland [doi>10.1145/1135777.1135792]

INDEX TERMS

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

General Terms:
Algorithms, Experimentation

Keywords:
opinion spam, product reviews, review spam