Cited By
View all- Burbano DHernandez-Alvarez M(2017)Identifying human trafficking patterns online2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM)10.1109/ETCM.2017.8247461(1-6)Online publication date: Oct-2017
Mining social network for extracting topic of textual conversations
Pages 232 - 237
Abstract
Developing internet usage and services urged play strong role for social network. Social networks are environment which uses internet as interface to provide relations between people, in the other word to interchange data and information between persons. Email and Instant Messengers are popular examples of them. Whereas these environments are continuously and instantly developing, revising and viewing by humans, they are good places for mining. In this paper, the topic of exchanged information between users in this type of networks will be our target. Our method is to use a hierarchical dictionary of semantically related topics and words that is mapped to a graph. Then extracted keywords from context of social network area compared to graph nodes and the dependency between them will be computed. This model can be used in many applications such as marketing, advertising and high-risk group detection.
References
[1]
Bigi, B., Brun, A., Haton, J., Smaili, K., and Zitouni, I. A comparative Study of Topic Identification on Newspaper and E-mail. In proc. String Processing and Information Retrieval Conference (SPIRE2001). 2001.
[2]
Bird, C., Gourley, A., Devanbu, P., Swaminathan, A., and Gertz, M. Mining Email Social Networks In Postgres. In proc. 3rd International Workshop on Mining Software Repositories. 2006.
[3]
Capasso, P., Cesarano, C., Picariello, A., and Sansone, L. A Semantic Topic Identification System for Document Retrieval on the Web. In proc. Of Web Age Information Management Workshops. 2006.
[4]
Domingos, P., Richardson, M. Mining the Network Value of Customers. In proc. Of 7th ACM SIGKDD. ACM Press, Canada, 2001, 57--61.
[5]
Jamali, M., and Abolhassani, H. Different Aspects of Social Network Analysis. In proc. IEEE/WIC/ACM International Conference on Web Intelligence. Hong Kong, 2006.
[6]
Lauw, H., Lim, E., Ng, T., and Pang, H. Social Network Discovery by Mining Spatio-Temporal Events. Computational and Mathematical Organization Theory, Vol. 11, 2005, 97--118.
[7]
Richardson, M., and Domingos, P. Mining Knowledge-Sharing Sites for Viral Marketing. In proc. Of 8th ACM SIGKDD. ACM Press, Canada, 2002, 61--70.
[8]
Staab, S., Domingos, P., Mike, P., Golbeck, J., Ding, L., Finin, T., Joshi, A., Nowak, A., and Vallacher, R. Social Networks Applied. IEEE Intelligent Systems, vol. 20, 2005, 80--93.
[9]
Yang, C. C., and Ng, T. D. Terrorism and Crime Related Weblog Social Network: Link, Content Analysis and Information Visualization. IEEE Intelligence and Security Informatics, 2007, 55--58.
[10]
Yang, W., Dia, J., Cheng, H., and Lin, H. Mining Social Networks for Targeted Advertising. In proc. Of 39th IEEE Hawaii International Conference on System Sciences. 2006.
[11]
Zhou, D., Councill, I., Zha, H., and Giles, C. Discovering Temporal Communities from Social Network Documents. In proc. 7th IEEE International Conference on Data Mining(ICDM 2007). 2007.
Index Terms
- Mining social network for extracting topic of textual conversations
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Published In
October 2008
733 pages
ISBN:9781605580463
DOI:10.1145/1456223
- Conference Chair:
- Lotfi A. Zadeh,
- General Chairs:
- Youakim Badr,
- Ajith Abraham,
- Yukio Ohsawa,
- Program Chairs:
- Richard Chbeir,
- Fernando Ferri,
- Mario Koeppen,
- Dominique Laurent
Copyright © 2008 ACM.
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- Institute of Electrical and Electronics Engineers France Section
- Laboratoire des Equipes Traitement des Images et du Signal
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- The International Fuzzy System Association
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- University of Cergy-Pontoise
- The World Federation of Soft Computing
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New York, NY, United States
Publication History
Published: 28 October 2008
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View all- Burbano DHernandez-Alvarez M(2017)Identifying human trafficking patterns online2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM)10.1109/ETCM.2017.8247461(1-6)Online publication date: Oct-2017
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