Identifying video spammers in online social networks

AIRWeb 2008 - Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web

Volumn , Issue , 2008, Pages 45-52

  Benevenuto, Fabricio ^a   Rodrigues, Tiago ^a   Almeida, Virgilio ^a   Almeida, Jussara ^a   Zhang, Chao ^b   Ross, Keith ^b  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b POLYTECHNIC UNIVERSITY   (United States)

Author keywords

Social network; Video response; Video spam

Indexed keywords

INFORMATION RETRIEVAL; INFORMATION SERVICES; INTERNET; LEARNING ALGORITHMS; ROBOT LEARNING;

LEGITIMATE USERS; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; SOCIAL BEHAVIORS; SOCIAL NETWORK; SPAM DETECTIONS; SPAMMERS; TEST COLLECTIONS; VIDEO RESPONSE; VIDEO SPAM; YOUTUBE;

SPAMMING;

EID: 63049136298     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1451983.1451996     Document Type: Conference Paper

References (24)

1. Alexa, http://www.alexa.com.
2. H. Aradhye, G. Myers, and J. Herson. Image analysis for efficient categorization of image-based spam e-mail. In Proc. of the Int'l Conf. on Document Analysis and Recognition (ICDAR), 2005.
3. R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval ACM Press / Addison-Wesley, 1999.
4. S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. In Proc. of World Wide Web Conf. (WWW), 1998.
5. C. Castillo, D. Donato, A. Gionis, V. Murdock, and F. Silvestři. Know your neighbors: Web spam detection using the web topology. In Int'l ACM SIGIR, pages 423-430, 2007.
6. M. Cha, H. Kwak, P. Rodriguez, Y. Ahn, and S. Moon. I tube, you tube, everybody tubes: Analyzing the world's largest user generated content video system. In Proc. of IMC, 2007.
7. S. Dorogovtsev and J. Mendes. Evolution of Networks: from Biological Nets to the Internet and WWW. Oxford University Press, 2003.
8. R. Fan, P. Chen, and C. Lin. Working set selection using the second order information for training svm. Journal of Machine Learning Research (JMLR), 6:1889-1918, 2005.
9. D. Fetterly, M. Manasse, and M. Najork. Spam, damn spam, and statistics: Using statistical analysis to locate spam web pages. In Proc. of WebDB, 2004.
10. P. Gill, M. Arlitt, Z. Li, and A. Mahanti. Youtube traffic characterization: A view from the edge. In Proc. of IMC, 2007.
11. L. Gomes, J. Almeida, V. Almeida, and W. Meira. Workload models of spam and legitimate e-mails. Performance Evaluation, 64:690-714, 2007.
12. L. Gomes, F. Castro, V. Almeida, J. Almeida, R. Almeida, and L. Bettencourt. Improving spam detection based on structural similarity. In Proc. of SRUTI, 2005.
13. Z. Gyöngyi, H. Garcia-Molina, and J. Pedersen. Combating web spam with trustrank. In Int'l. Conf. on Very Large Data Bases, pages 576-587, 2004.
14. P. Heymann, G. Koutrika, and H. Garcia-Molina. Fighting spam on social web sites: A survey of approaches and future challenges. IEEE Internet Computing, 11(6):36-45, 2007.
15. R. Jain. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley and Sons, INC, 1991.
16. J. Kleinberg. Hubs, authorities, and communities. ACM Computing Surveys, 31, 1999.
17. G. Koutrika, F. Effendi, Z. Gyöngyi, P. Heymann, and H. Garcia-Molina. Combating spam in tagging systems. In Proc. of AIRWeb, 2007.
18. M. Shannon. Shaking hands, kissing babies, and...blogging? Communications of the ACM, 50, 2007.
19. A. Thomason. Blog spam: A review. In Proc. of Conf. on Email and Anti-Spam (CEAS), 2007.
20. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun. Large margin methods for structured and interdependent output variables. Journal of Machine Learning Research (JMLR), 6:1453-1484, 2005.
21. I. Witten and E. Frank. Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, 2005.
22. C. Wu, K. Cheng, Q. Zhu, and Y. Wu. Using visual features for anti-spam filtering. In Proc. of IEEE Int'l Conf. on Image Processing (ICIP), 2005.
23. Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In Proc. of the Int'l Conf. on Machine Learning (ICML), 1997.
24. L. Yu and H. Liu. Redundancy based feature selection for microarray data. In Proc. of ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining (KDD), 2004.