Abstract
The study of extremist groups and their interaction is a crucial task in order to maintain homeland security and peace. Tools such as social networks analysis and text mining have contributed to their understanding in order to develop counter-terrorism applications. This work addresses the topic-based community key-members extraction problem, for which our method combines both text mining and social network analysis techniques. This is achieved by first applying latent Dirichlet allocation to build two topic-based social networks in online forums: one social network oriented towards the thread creator point-of-view, and the other is oriented towards the repliers of the overall forum. Then, by using different network analysis measures, topic-based key members are evaluated using as benchmark a social network built a plain representation of the network of posts. Experiments were successfully performed using an English language based forum available in the Dark Web portal.
- A. Abbasi and H. Chen. Applying authorship analysis to extremist-group web forum messages. IEEE Intelligent Systems, 20(5):67--75, 2005. Google ScholarDigital Library
- A. Abbasi, H. Chen, and A. Salem. Sentiment analysis in multiple languages: Feature selection for opinion classification in web forums. ACM Trans. Inf. Syst., 26(3):1--34, 2008. Google ScholarDigital Library
- D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. The Journal of Machine Learning . . . , Jan 2003. Google ScholarDigital Library
- A. Bourhis, L. Dubé, and R. Jacob. . . . The success of virtual communities of practice: The leadership factor. The Electronic Journal of Knowledge . . . , Jan 2005.Google Scholar
- R. B. Bradford. Application of latent semantic indexing in generating graphs of terrorist networks. pages 674--675, 2006. Google ScholarDigital Library
- L. C. Freeman. Centrality in social networks conceptual clarification. Social Networks, 1(3):215--239, 1978.Google ScholarCross Ref
- W. Kim, O.-R. Jeong, and S.-W. Lee. On social web sites. Information Systems, 35(2):215--236, 2010. Google ScholarDigital Library
- J. M. Kleinberg. Authoritative sources in a hyperlinked environment. J. ACM, 46(5):604--632, 1999. Google ScholarDigital Library
- M. Kosonen. Knowledge sharing in virtual communities -- a review of the empirical research. Int. J. Web Based Communities, 5(2):144--163, 2009. Google ScholarDigital Library
- H. Kwak, Y. Choi, Y.-H. Eom, H. Jeong, and S. Moon. Mining communities in networks: a solution for consistency and its evaluation. pages 301--314, 2009. Google ScholarDigital Library
- A. McCallum, X. Wang, and A. Corrada-Emmanuel. Topic and role discovery in social networks with experiments on enron and academic email. Journal of Artificial . . . , Jan 2007. Google ScholarDigital Library
- R. D. Nolker and L. Zhou. Social computing and weighting to identify member roles in online communities. Web Intelligence, IEEE / WIC / ACM International Conference on, 0:87--93, 2005. Google ScholarDigital Library
- N. Pathak, C. Delong, A. Banerjee, and K. Erickson. Social topic models for community extraction. Aug 2008.Google Scholar
- U. Pfeil and P. Zaphiris. Investigating social network patterns within an empathic online community for older people. Computers in Human Behavior, 25(5):1139--1155, 2009. Google ScholarDigital Library
- X. H. Phang and C. Nguyen. Gibbslda++, 2008.Google Scholar
- C. E. Porter. A typology of virtual communities: A multi-disciplinary foundation for future research. Journal of Computer-Mediated Communication, 10(1):00, 2004.Google ScholarCross Ref
- G. Probst and S. Borzillo. Why communities of practice succeed and why they fail. European Management Journal, 26(5):335--347, 2008.Google ScholarCross Ref
- E. Reid, J. Qin, Y. Zhou, G. Lai, M. Sageman, G. Weimann, and H. Chen. Collecting and analyzing the presence of terrorists on the web: A case study of jihad websites. pages 402--411, 2005. Google ScholarDigital Library
- S. A. Ríos, F. Aguilera, and L. Guerrero. Virtual communities of practice's purpose evolution analysis using a concept-based mining approach. Knowledge-Based and Intelligent Information and Engineering Systems, 2:480--489, 2009. Google ScholarDigital Library
- M. Sageman. A strategy for fighting international islamist terrorists. ANNALS of the American Academy of Political and Social Science, 618(1):223--231, 2008.Google ScholarCross Ref
- G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, Vol. 18(11):613--620, 1975. Google ScholarDigital Library
- S. Wasserman and K. Faust. Social Network Analysis: Methods and Applications. 1994.Google Scholar
- D. Xing and M. Girolami. Employing latent dirichlet allocation for fraud detection in telecommunications. Pattern Recognition Letters, Vol. 28(13):1727--1734, 2007. Google ScholarDigital Library
- J. Xu and H. Chen. Crimenet explorer: a framework for criminal network knowledge discovery. ACM Transactions on Information Systems (TOIS), Jan 2005. Aqui sale bien el blockmodeling. Google ScholarDigital Library
- J. Xu and H. Chen. The topology of dark networks. Commun. ACM, 51(10):58--65, 2008. Google ScholarDigital Library
- L. Yang, F. Liu, J. Kizza, and R. Ege. Discovering topics from dark websites. In CICS '09: IEEE Symposium on Computational Intelligence in Cyber Security., pages 175--179. IEEE, 2009.Google ScholarCross Ref
- L. Yang, F. Liu, J. Kizza, and R. Ege. Discovering topics from dark websites. Computational Intelligence in Cyber Security, 2009. CICS '09. IEEE Symposium on, pages 175--179, 2009.Google ScholarCross Ref
- Y. Zhang, S. Zeng, L. Fan, Y. Dang, C. A. Larson, and H. Chen. Dark web forums portal: searching and analyzing jihadist forums. pages 71--76, 2009. Google ScholarDigital Library
- Y. Zhou, E. Reid, J. Qin, H. Chen, and G. Lai. Us domestic extremist groups on the web: Link and content analysis. IEEE Intelligent Systems, 20(5):44--51, 2005. Google ScholarDigital Library
Index Terms
- Topic-based social network analysis for virtual communities of interests in the dark web
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