Spotting fake reviewer groups in consumer reviews

WWW'12 - Proceedings of the 21st Annual Conference on World Wide Web

Volumn , Issue , 2012, Pages 191-200

  Mukherjee, Arjun ^a   Liu, Bing ^a   Glance, Natalie ^b  

^a 1120 Science and Engineering Offices   (United States)

^b GOOGLE INC   (United States)

Author keywords

Fake review detection; Group opinion spam; Opinion spam

Indexed keywords

BEHAVIORAL MODEL; COLLABORATIVE SETTINGS; DATA SETS; FREQUENT ITEMSET MINING; GROUP OPINION SPAM; LEARNING TO RANK; OPINION SPAM; PRODUCT REVIEWS; RELATION MODELS; SOCIAL MEDIA; SPAM DETECTION; SUPERVISED CLASSIFICATION; USER EXPERIENCE;

MINING; PROFITABILITY; SUPERVISED LEARNING;

WORLD WIDE WEB;

EID: 84860868145     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2187836.2187863     Document Type: Conference Paper

References (37)

1. Agrawal, R. and Srikant, R. Fast algorithms for mining association rules. VLDB. 1994.
2. Benevenuto, F., Rodrigues, T., Almeida, V., Almeida, J, Gonvalves, M. A. Detecting spammers and content promoters in online video social networks. SIGIR. 2009.
3. Burges, C., Shaked, T., Renshaw, E. Lazier, A. Deeds, M., Hamilton, N., Hullender., G. Learning to rank using gradient descent. ICML. 2005.
4. Castillo, C., Davison, B. Adversarial Web Search Foundations and Trends in Information Retrieval, 5, 2010.
5. Castillo, C., Donato, D., Becchetti, L., Boldi, P., Leonardi, S., Santini, M., and Vigna, S. 2006. A reference collection for web spam. SIGIR Forum 40, 2, 11-24, S. 2006.
6. Chirita, P.A., Diederich, J., and Nejdl, W. MailRank: using ranking for spam detection. CIKM. 2005.
7. Douceur, J. R. The sybil attack. IPTPS Workshop. 2002.
8. Eagle, N. and Pentland, A. Reality Mining: Sensing Complex Social Systems. Personal and Ubiquitous Computing. 2005.
9. Fayyad, U. M. and Irani, K. B. Multi-interval discretization of continuous-valued attributes for classification learning. IJCAI. 1993.
10. Fleiss, J. L. Measuring nominal scale agreement among many raters. Psychological Bulletin, 76(5), pp. 378-382, 1971.
11. Freund, Y., Iyer, R., Schapire, R. and Singer, Y. An efficient boosting algorithm for combining preference. JMLR. 2003.
12. Heath, M. T., Scientific Computing: An Introductory Survey. McGrawHill, New York. Second edition. 2002.
13. Hsu, W., Dutta, D., Helmy, A. Mining Behavioral Groups in Large Wireless LANs. MobiCom. 2007.
14. Jindal, N. and Liu, B. Opinion spam and analysis. WSDM. 2008.
15. Jindal, N., Liu, B. and Lim, E. P. Finding Unusual Review Patterns Using Unexpected Rules. CIKM. 2010.
16. Joachims, T. Making large-scale support vector machine learning practical. Advances in Kernel Methods. MIT Press. 1999.
17. Joachims, T. Optimizing Search Engines Using Clickthrough Data. KDD. 2002.
18. Kim, S.M., Pantel, P., Chklovski, T. and Pennacchiotti, M. Automatically assessing review helpfulness. EMNLP. 2006.
19. Kleinberg, J. M. Authoritative sources in a hyperlinked environment. ACM-SIAM SODA, 1998.
20. Kolari, P., Java, A., Finin, T., Oates, T., Joshi, A. Detecting Spam Blogs: A Machine Learning Approach. AAAI. 2006.
21. Koutrika, G., Effendi, F. A., Gyöngyi, Z., Heymann, P., and H. Garcia-Molina. Combating spam in tagging systems. AIRWeb. 2007.
22. Landis, J. R. and Koch, G. G. The measurement of observer agreement for categorical data. Biometrics, 33, 159-174, 1977.
23. Li, F., Huang, M., Yang, Y. and Zhu, X. Learning to identify review Spam. IJCAI. 2011.
24. Lim, E., Nguyen, V. A., Jindal, N., Liu, B., and Lauw, H. Detecting Product Review Spammers Using Rating Behavior. CIKM. 2010.
25. Liu, J., Cao, Y., Lin, C., Huang, Zhou, M. Low-quality product review detection in opinion summarization. EMNLP, 2007.
26. Liu, T-Y. Learning to Rank for Information Retrieval. Foundations and Trends in Information Retrieval 3(3): 225-331. 2009.
27. Markines, B., Cattuto, C., and Menczer, F. Social spam detection. AIRWeb. 2009.
28. Martinez-Romo, J. and Araujo, A. Web Spam Identification Through Language Model Analysis. AIRWeb. 2009.
29. Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N. Detecting Group Review Spam. WWW. 2011. (Poster paper).
30. Ntoulas, A., Najork, M., Manasse M., Fetterly, D. Detecting Spam Web Pages through Content Analysis. WWW'2006.
31. Ott, M., Choi, Y., Cardie, C., Hancock, J. Finding Deceptive Opinion Spam by Any Stretch of the Imagination. ACL. 2011.
32. Wang, G., Xie, S., Liu, B., and Yu, P. S. Review Graph based Online Store Review Spammer Detection. ICDM. 2011.
33. Wang, Y. Ma, M. Niu, Y. and Chen, H. Spam Double-Funnel: Connecting Web Spammers with Advertisers. WWW'2007.
34. Wu, G., Greene, D., Smyth, B. and Cunningham, P. 2010. Distortion as a validation criterion in the identification of suspicious reviews. Technical report, UCD-CSI-2010-04, University College Dublin.
35. Wu, B., Goel V. & Davison, B. D. Topical TrustRank: using topicality to combat Web spam. WWW. 2006.
36. Yan, F., Jiang, J., Lu, Y., Luo, Q., Zhang, M. Community discovery based on social actors'interests & social relationships. SKG. 2008.
37. Zhang, Z. and Varadarajan, B. Utility scoring of product reviews. CIKM. 2006.