Finding the hidden hands: A case study of detecting organized posters and promoters in SINA weibo

China Communications

Volumn 12, Issue 11, 2015, Pages 143-155

  Xiang, Wang ^a   Zhilin, Zhang ^b   Xiang, Yu ^a   Yan, Jia ^a   Bin, Zhou ^a   Shasha, Li ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

^b SIMON FRASER UNIVERSITY   (Canada)

Author keywords

internet water army; microblogging; online paid posters; organized posters; promoter

Indexed keywords

EID: 84961575300     PISSN: 16735447     EISSN: None     Source Type: Journal    
DOI: 10.1109/CC.2015.7366237     Document Type: Article

References (28)

1. Cheng Chen, KuiWu, VenkateshSrinivasan, and Xudong Zhang. Battling the internet water army: Detection of hidden paid posters. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pages 116-120. ACM, 2013.
2. Zi Chu, Steven Gianvecchio, Haining Wang, and Sushil Jajodia. Who is tweeting on twitter: human, bot, or cyborg? In Proceedings of the 26th annual computer security applications conference, pages 21-30. ACM, 2010.
3. Nitin Jindal and Bing Liu. Opinion spam and analysis. In Proceedings of the 2008 International Conference on Web Search and Data Mining, pages 219-230. ACM, 2008.
4. Myle Ott, Yejin Choi, Claire Cardie, and Jeffrey T Hancock. Finding deceptive opinion spam by any stretch of the imagination. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, pages 309-319. Association for Computational Linguistics, 2011.
5. Chao Yang, Robert Harkreader, Jialong Zhang, Seungwon Shin, and Guofei Gu. Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In Proceedings of the 21st international conference on World Wide Web, pages 71-80. ACM, 2012.
6. Chris Grier, Kurt Thomas, Vern Paxson, and Michael Zhang. @ spam: the underground on 140 characters or less. In Proceedings of the 17th ACM conference on Computer and communications security, pages 27-37. ACM, 2010.
7. Yubao Zhang Xin Ruan Haining Wang Hui Wang. What scale of audience a campaign can reach in what price. In 2014 IEEE International Conference on Computer Communications (InfoCOM'14), 2014.
8. Hongyu Gao, Jun Hu, Christo Wilson, Zhichun Li, Yan Chen, and Ben Y Zhao. Detecting and characterizing social spam campaigns. In Proceedings of the 10th ACM SIGCOMM conference on Internet measurement, pages 35-47. ACM, 2010.
9. Kurt Thomas, Chris Grier, Justin Ma, Vern Paxson, and Dawn Song. Design and evaluation of a realtime url spam filtering service. In Security and Privacy (SP), 2011 IEEE Symposium on, pages 447-462. IEEE, 2011.
10. Zhaoyun DING, Yan JIA, Bin Zhou, and Yi HAN. Mining topical influencers based on the multirelational network in micro-blogging sites. China Communications, 10(1):93-104, 2013.
11. Adam Thomason. Blog spam: A review. In CEAS, 2007.
12. Pranam Kolari, Akshay Java, Tim Finin, Tim Oates, and Anupam Joshi. Detecting spam blogs: A machine learning approach. In PROCEEDINGS OFTHENATIONALCONFERENCEONARTIFICIALI NTELLIGENCE, volume21, page1351. MenloPark, CA;Cambridge, MA;London; AAAI Press; MIT Press; 1999, 2006.
13. Enrico Blanzieri and Anton Br yl. A survey of learning-based techniques of email spam filtering. Artificial Intelligence Review, 29(1):63-92, 2008.
14. Ion Androutsopoulos, John Koutsias, Konstantinos V Chandrinos, and Constantine D Spyropoulos. An experimental comparison of naive bayesian and keyword-based anti-spam filtering with personal e-mail messages. In Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, pages 160-167. ACM, 2000.
15. Dennis Fetterly, Mark Manasse, and Marc Najork. Spam, damn spam, and statistics: Using statistical analysis to locate spam web pages. In Proceedings of the 7th International Workshop on the Web and Databases: colocated with ACM SIGMOD/PODS 2004, pages 1-6. ACM, 2004.
16. Fabricio Benevenuto, Fernando Duarte, Tiago Rodrigues, Virgilio AF Almeida, Jussara M Almeida, and Keith W Ross. Understanding video interactions in youtube. In Proceedings of the 16th ACM international conference on Multimedia, pages 761-764. ACM, 2008.
17. Fabricio Benevenuto, Tiago Rodrigues, Virgilio Almeida, Jussara Almeida, Chao Zhang, and Keith Ross. Identifying video spammers in online social networks. In Proceedings of the 4th international workshop on Adversarial information retrieval on the web, pages 45-52. ACM, 2008.
18. Harris Drucker, S Wu, and Vladimir N Vapnik. Support vector machines for spam categorization. Neural Networks, IEEE Transactions on, 10(5):1048-1054, 1999.
19. Zoltán Gy?ngyi, Hector Garcia-Molina, and Jan Pedersen. Combating web spam with trustrank. In Proceedings of the Thirtieth international conference on Very large data bases-Volume 30, pages 576-587. VLDB Endowment, 2004.
20. Kurt Thomas, Chris Grier, Dawn Song, and Vern Paxson. Suspended accounts in retrospect: an analysis of twitter spam. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference, pages 243-258. ACM, 2011.
21. Kyumin Lee, James Caverlee, and Steve Webb. Uncovering social spammers: social honeypots+ machine learning. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 435-442. ACM, 2010.
22. M McCord and M Chuah. Spam detection on twitter using traditional classifiers. In Autonomic and trusted computing, pages 175-186. Springer, 2011.
23. Kun Wang, Yang Xiao, and Zhen Xiao. Detection of internet water army in social network. In 2014 International Conference on Computer, Communications and Information Technology (CCIT 2014). Atlantis Press, 2014.
24. Ke Zeng, Xiao Wang, Qingpeng Zhang, Xinzhan Zhang, and Fei-Yue Wang. Behavior modeling of internet water army in online forums. In World Congress, volume 19, pages 9858-9863, 2014.
25. Gerard Salton, Anita Wong, and Chung-Shu Yang. A vector space model for automatic indexing. Communications of the ACM, 18(11):613-620, 1975.
26. Fabricio Benevenuto, Gabriel Magno, Tiago Rodrigues, and Virgilio Almeida. Detecting spammers on twitter. In Collaboration, electronic messaging, anti-abuse and spam conference (CEAS), volume 6, page 12, 2010.
27. Chih-Chung Chang and Chih-Jen Lin. Libsvm: A library for support vector machines. ACM Trans. Intell. Syst. Technol., 2(3):27:1-27:27, May 2011.
28. Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Comput. Surv., 34(1):1-47, March 2002.