Detecting Campaign Promoters on Twitter Using Markov Random Fields

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn 2015-January, Issue January, 2014, Pages 290-299

  Li, Huayi ^a   Mukherjee, Arjun ^c   Liu, Bing ^a   Kornfield, Rachel ^b   Emery, Sherry ^b  

^a Department of Computer Science ^*  (United States)

^b UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^c University of Houston   (United States)

Author keywords

Campaign Promoter; Markov Random Fields

Indexed keywords

HEALTH; MAGNETORHEOLOGICAL FLUIDS; MARKETING; MARKOV PROCESSES; SOCIAL NETWORKING (ONLINE);

CAMPAIGN PROMOTER; GENERAL PUBLICS; INTERNET MARKETING; MARKOV RANDOM FIELDS; PROMOTIONAL CAMPAIGN; PUBLIC INFORMATION; REAL LIFE DATASETS; SOCIAL MEDIA PLATFORMS;

DATA MINING;

EID: 84936947672     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2014.59     Document Type: Conference Paper

References (37)

1. B. Liu, Sentiment Analysis and Opinion Mining, ser. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, 2012.
2. V. Qazvinian, E. Rosengren, D. R. Radev, and Q. Mei, " Rumor has it: Identifying Misinformation in Microblogs," in EMNLP, 2011, pp. 1589-1599.
3. M. Gupta, P. Zhao, and J. Han, "Evaluating Event Credibility on Twitter, " in SDM, 2012, pp. 153-164.
4. F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida, " Detecting spammers on twitter," in Collaboration, electronic messaging, antiabuse and spam conference (CEAS), vol. 6, 2010.
5. F. Benevenuto, T. Rodrigues, V. A. F. Almeida, J. M. Almeida, and M. A. Gonçalves, " Detecting spammers and content promoters in online video social networks," in SIGIR, 2009, pp. 620-627.
6. C. Grier, K. Thomas, V. Paxson, and C. M. Zhang, "@spam: the underground on 140 characters or less," in ACM Conference on Computer and Communications Security, 2010, pp. 27-37.
7. K. Thomas, C. Grier, D. Song, and V. Paxson, "Suspended accounts in retrospect: an analysis of twitter spam, " in Internet Measurement Conference, 2011, pp. 243-258.
8. J. Bollen, H. Mao, and A. Pepe, "Modeling Public Mood and Emotion: Twitter Senti ment and Socio-Economic Phenomena," in ICWSM, 2011.
9. J. Bollen, H. Mao, and X.-J. Zeng, "Twitter mood predicts the stock market, " J. Comput. Science, vol. 2, no. 1, pp. 1-8, 2011.
10. E. J. Ruiz, V. Hristidis, C. Castillo, A. Gionis, and A. Jaimes, " Correlating financial time series with micro-blogging activity," in WSDM, 2012, pp. 513-522.
11. A. Tumas jan, T. O. Sprenger, P. G. Sandner, and I. M. Welpe, " Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment," in ICWSM, 2010.
12. S. Asur and B. A. Huberman, "Predicting the Future with Social Media, " in Web Intelligence, 2010, pp. 492-499.
13. X. Liu, K. Tang, J. Hancock, J. Han, M. Song, R. Xu, and B. Pokorny, " A Text Cube Approach to Human, Social and Cultural Behavior in the Twitter Stream," in SBP, 2013, pp. 321-330.
14. X. Shuai, Y. Ding, J. R. Busemeyer, S. Chen, Y. Sun, and J. Tang, " Modeling Indirect Influence on Twitter," Int. J. Semantic Web Inf. Syst., vol. 8, no. 4, pp. 20-36, 2012.
15. N. Jindal and B. Liu, "Opinion spam and analysis, " in WSDM, 2008, pp. 219-230.
16. A. Mukher jee, V. Venkataraman, B. Liu, and N. S. Glance, " What yelp fake review filter might be doing?" in ICWSM, 2013.
17. C. Phua,V. Lee,K. Smith,R. Gayler A comprehensive survey of data mining-based fraud detection research arXiv preprint arXiv: 1009.6119 2010.
18. M. J. Paul and M. Dredze, "You Are What You Tweet: Analyzing Twitter for Public Health, " in ICWSM, 2011.
19. G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, and R. Ghosh, " Exploiting Burstiness in Reviews for Review Spammer Detection," in ICWSM, 2013.
20. A. Mukherjee, A. Kumar, B. Liu, J. Wang, M. Hsu, M. Castellanos, and R. Ghosh, " Spotting opinion spammers using behavioral footprints," in KDD, 2013, pp. 632-640.
21. K. P. Murphy, Y. Weiss, and M. I. Jordan, "Loopy Belief Propagation for Approximate Inference: An Empirical Study, " in UAI, 1999, pp. 467-475.
22. S. Ghosh, B. Viswanath, F. Kooti, N. K. Sharma, G. Korlam, F. Benevenuto, N. Ganguly, and P. K. Gummadi, " Understanding and combating link farming in the twitter social network." in WWW, A. Mille, F. L. Gandon, J. Misselis, M. Rabinovich, and S. St aab, Eds. ACM, 2012, pp. 61-70. [Online]. Available: http://dblp.unitrier. De/db/conf/www/www2012.html#GhoshVKSKBGG12
23. C. Yang, R. C. Harkreader, J. Zhang, S. Shin, and G. Gu, " Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter," in WWW, 2012, pp. 71-80.
24. H. Gao, J. Hu, C. Wilson, Z. Li, Y. Chen, and B. Y. Zhao, " Detecting and characterizing social spam campaigns," in ACM Conference on Computer and Communications Security, 2010, pp. 681-683.
25. K. Lee, J. Caverlee, Z. Cheng, and D. Z. Sui, "Content-driv en detection of campaigns in social media," in CIKM, 2011, pp. 551-556.
26. X. Zhang, S. Zhu, and W. Liang, "Detecting spam and promoting campaigns in the twitter social network, " in ICDM, 2012, pp. 1194-1199.
27. S. Pandit, D. H. Chau, S. Wang, and C. Faloutsos, " Netprobe: a fast and scalable system for fraud detection in online auction networks," in WWW, 2007, pp. 201-210.
28. M. McGlohon, S. Bay, M. G. Anderle, D. M. Steier, and C. Faloutsos, " Snare: a link analytic system for graph labeling and risk detection," in KDD, 2009, pp. 1265-1274.
29. L. Akoglu, R. Chandy, and C. Faloutsos, " Opinion Fraud Detection in Online Reviews by Network Effects," in ICWSM, 2013.
30. D. Gruhl, R. Guha, D. Liben-nowell, and A. Tomkins, " Information Diffusion through Blogspace," in WWW. ACM Press, 2004, pp. 491-501.
31. D. M. Romero, B. Meeder, and J. M. Kleinberg, "Differences in the mechanics of information diffusion across topics : idioms, political hashtags, and complex contagion on twitter," in WWW, 2011, pp. 695-704.
32. M. Rahman, B. Carbunar, J. Ballesteros, G. Burri, and D. H. Chau., " Turning the tide: Curbing deceptive yelp behaviors," in SIAM, 2014.
33. J. Pearl, "Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach, " in AAAI, 1982, pp. 133-136.
34. J. S. Yedidi a, W. T. Freeman, and Y. Weiss, " Generalized Belief Propagation," in NIPS, 2000, pp. 689-695.
35. P. Du, W. A. Kibbe, and S. M. Lin, " Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching," Bioinformatics, vol. 22, no. 17, pp. 2059-2065, 2006.
36. D. Gayo-Avello and D. J. Brenes, "Overcoming Spammers in Twitter-A Tale of Five Algorithms, " in 1st Spanish Conference on Information Retrieval, Madrid, Spain, 2010.
37. P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Gallagher, and T. Eliassi-Rad, " Collective classification in network data," AI Magazine, vol. 29, no. 3, pp. 93-106, 2008.