A social-spam detection framework

ACM International Conference Proceeding Series

Volumn , Issue , 2011, Pages 46-54

  Wang, De ^a   Irani, Danesh ^a   Pu, Calton ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Classification; Detection; Framework; Social spam

Indexed keywords

BEHAVIORAL ANALYSIS; DETECTION FRAMEWORK; EXPERIMENTAL STUDIES; FACEBOOK; FILTERING TECHNIQUE; FRAMEWORK; REAL DATA SETS; SOCIAL NETWORKS; SOCIAL-SPAM; SPAM DETECTION; SPAM FILTER; SPAMMERS;

CLASSIFICATION (OF INFORMATION); ERROR DETECTION; SOCIAL NETWORKING (ONLINE);

INTERNET;

EID: 80053654994     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2030376.2030382     Document Type: Conference Paper

References (31)

1. http://hootsuite.com/.
2. http://tweetdeck.com/.
3. http://code.google.com/apis/opensocial/.
4. F. Benevenuto, G. Magno, T. Rodrigues, and V. Almeida. Detecting spammers on twitter. In Proceedings of the Seventh annual Collaboration, Electronic messaging, AntiAbuse and Spam Conference(CEAS 2010), 2010.
5. B. Byun, C. Lee, S. Webb, D. Irani, and C. Pu. An anti-spam filter combination framework for text-and-image emails through incremental learning. In Proceedings of the the Sixth Conference on Email and Anti-Spam (CEAS 2009), 2009.
6. X. Carreras and L. Marquez. Boosting trees for anti-spam email filtering. Arxiv preprint, 2001.
7. J. Caverlee, L. Liu, and S. Webb. Socialtrust: tamper-resilient trust establishment in online communities. In Proceedings of the 8th ACM/IEEE-CS joint conference on Digitial libraries, 2008.
8. J. Caverlee and S. Webb. A large-scale study of MySpace: Observations and implications for online social networks. Proceedings of the International Conference on Weblogs and Social Media, 8, 2008.
9. H. Drucker, D. Wu, and V. Vapnik. Support vector machines for spam categorization. Neural Networks, IEEE Transactions on, 10(5):1048-1054, 1999.
10. J. Friedman, T. Hastie, and R. Tibshirani. Additive logistic regression: a statistical view of boosting. The annals of statistics, 28(2):337-407, 2000.
11. Gosier and Guadeloupe. Social networks as an attack platform: Facebook case study. In Proceedings of the Eighth International Conference on Networks, 2009.
12. Z. Gyongyi, H. Garcia-Monlina, and J. Pedersen. Combating web spam with trustrank. In Proceeding of the Thirtieth international conference on Very large data bases, volume 30, 2004.
13. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. Witten. The WEKA data mining software. ACM SIGKDD Explorations Newsletter, 11(1):10-18, 2009.
14. J. Hirai, S. Raghavan, H. Garcia-Molina, and A. Paepcke. WebBase: A repository of web pages. Computer Networks, 33(1-6):277-293, 2000.
15. D. Irani, S. Webb, J. Giffin, and C. Pu. Evolutionary study of phishing. eCrime Researchers Summit, 2008, pages 1-10, 2008.
16. D. Irani, S. Webb, and C. Pu. Study of static classification of social spam profiles in myspace. In Proceedings of the International AAAI Conference on Weblogs and Social Media, May 2010.
17. D. Irani, S. Webb, C. Pu, and K. Li. Study of trend-stuffing on twitter through text classification. In Collaboration, Electronic messaging, Anti-Abuse and Spam Conference (CEAS 2010), 2010.
18. C. Kreibich, C. Kanich, K. Levchenko, B. Enright, G. Voelker, V. Paxson, and S. Savage. On the spam campaign trail. In Proceedings of the 1st Usenix Workshop on Large-Scale Exploits and Emergent Threats, pages 1-9. USENIX Association, 2008.
19. M. Learmonth. Twitter getting serious about spam issue. http://adage.com/article/digital/ digital-marketing-twitter-spam-issue/142800/, 2010.
20. E. Lex, C. Seifert, M. Granitzer, and A. Juffinger. Efficient cross-domain classification of weblogs. International Journal of Intelligent Computing Research, 1(1), 2010.
21. C. Pu and S. Webb. Observed trends in spam construction techniques: a case study of spam evolution. In Proceedings of the Third Conference on Email and Anti-Spam (CEAS 2006), 2006.
22. C. Pu, S.Webb, O. Kolesnikov, W. Lee, and R. Lipton. Towards the integration of diverse spam filtering techniques. In Proceedings of the IEEE International Conference on Granular Computing (GrC06), pages 17-20.
23. M. Sahami, S. Dumais, D. Heckerman, and E. Horvitz. A Bayesian approach to filtering junk e-mail. In Learning for Text Categorization: Papers from the 1998 workshop, volume 62, pages 98-05. Madison, Wisconsin: AAAI Technical Report WS-98-05, 1998.
24. T. Stein, E. Chen, and K. Mangla. Facebook immune system. In Proceedings of the forth ACM EuroSys Workshop on Social Network Systems(SNS2011), 2011.
25. K. Thomas, C. Grier, J. Ma, V. Paxson, and D. Song. Design and evaluation of a real-time url spam filtering service. In Proceedings of the IEEE Symposium on Security and Privacy, 2011.
26. E. Voorhees, D. Harman, N. I. of Standards, and T. (US). TREC: Experiment and evaluation in information retrieval. MIT press USA, 2005.
27. P. Wang, C. Domeniconi, and J. Hu. Cross-domain text classification using wikipedia. IEEE Intelligent Informatics Bulletin, 9(1), 2008.
28. S. Webb, J. Caverlee, and C. Pu. Introducing the webb spam corpus: Using email spam to identify web spam automatically. In Proceedings of the Third Conference on Email and Anti-Spam (CEAS 2006), 2006.
29. S. Webb, J. Caverlee, and C. Pu. Characterizing web spam using content and http session analysis. In Proceedings of the Fourth Conference on Email and Anti-Spam (CEAS 2007), pages 84-89, August 2007.
30. S. Webb, J. Caverlee, and C. Pu. Predicting web spam with http session information. In Proceedings of the Seventeenth Conference on Information and Knowledge Management (CIKM 2008), October 2008.
31. S. Webb, J. Caverlee, and C. Pu. Social honeypots: Making friends with a spammer near you. In Proceedings of the Fifth Conference on Email and Anti-Spam (CEAS 2008), 2008.