Mining Social Data to Extract Intellectual Knowledge
Автор: Muhammad Mahbubur Rahman
Журнал: International Journal of Intelligent Systems and Applications(IJISA) @ijisa
Статья в выпуске: 10 vol.4, 2012 года.
Бесплатный доступ
Social data mining is an interesting phe-nomenon which colligates different sources of social data to extract information. This information can be used in relationship prediction, decision making, pattern recognition, social mapping, responsibility distribution and many other applications. This paper presents a systematical data mining architecture to mine intellectual knowledge from social data. In this research, we use social networking site facebook as primary data source. We collect different attributes such as about me, comments, wall post and age from facebook as raw data and use advanced data mining approaches to excavate intellectual knowledge. We also analyze our mined knowledge with comparison for possible usages like as human behavior prediction, pattern recognition, job responsibility distribution, decision making and product promoting.
Social Computing, Data Mining, Face-book, Intellectual Knowledge
Короткий адрес: https://sciup.org/15010314
IDR: 15010314
Список литературы Mining Social Data to Extract Intellectual Knowledge
- Chin, A. and Chignell, M., “Finding Evidence of Community from Blogging Co-Citations: A Social Network Analytic Approach” In Proceedings of the IADIS International Conference on Web Based Communities 2006, San Sebastian, Spain, February 26-28, 2006.
- Memic H. and Joldic A., “A more comprehensive activity analysis of standard online social network-ing functionalities”, International Conference on Software Technology and Engineering (ICSTE), 3-5 Oct. 2010.
- Alim S. , Abdul-Rahman R. , Neagu D. and Ridley M., “Data retrieval from online social network pro-files for social engineering applications”, Interna-tional Conference for Internet Technology and Se-cured Transactions, 9-12 Nov. 2009.
- Bo Xu and Lu Liu, “Information diffusion through online social networks”, International Conference on Emergency Management and Management Sciences (ICEMMS), 8-10 Aug. 2010.
- Yu Zhang, Zhaoqing Wang and Chaolun Xia,” Identifying Key Users for Targeted Marketing by Mining Online Social Network”, 24th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 644 – 649, 20-23 April 2010.
- Cross R. and Parker A. , “The Hidden Power of Social Networks, ” Harvard University Press, 2004.
- Turoff M., Hiltz. S. R., Cho H. K., Li Z., and Wang, Y., “Social Decision Support Systems (SDSS)” 35th Hawaii International Conference on System Sciences, pp. 1-10, 2002.
- Nasrullah Memon and Henrik Legind Larsen, “Structural Analysis and Mathematical Methods for Destabilizing Terrorist Networks Using Inves-tigative Data Mining”, Lecture Notes in Advanced Data Mining and Applications of Computer Sci-ence, pp. 1037-1048, Springer, 2006.
- Li Ding, Tim Finin and Anupam Joshi, “Analyzing Social Networks on the Semantic Web”, IEEE In telligent Systems (Trends & Controversies), volume 8, number 6, Nov/Dec 2004.
- I-Hsien Ting, Hui-Ju Wu and Pei-Shan Chang, “Analyzing Multi-Source Social Data for Extract-ing and Mining Social Networks”, pp.815-820, In-ternational Conference on Computational Science and Engineering, 2009.
- Carson Kai-Sang Leung and Christopher L. Carmi-chael, “Exploring Social Networks: A Frequent Pattern Visualization Approach”, pp. 419-424, IEEE International Conference on Social Compu-ting, 2010.
- I. Indratmo and J. Vassileva, “Social interaction history: A framework for supporting exploration of social information spaces,” pp. 538–545, Proc. So-cialCom 2009.
- A. Mislove, B. Viswanath, K.P. Gummadi, and P. Druschel, “You are who you know: Inferring user profiles in online social networks,” pp. 251–260, Proc. WSDM 2010.
- B.-Q. Vuong, E.-P. Lim, et al., “On ranking con-troversies in Wikipedia: Models and evaluation,” pp. 171-182, Proc. WSDM 2008.
- Baojun Qiu, Kristinka Ivanovay, John Yeny, and Peng Liuy, “Behavior Evolution and Event-driven Growth Dynamics in Social Networks”, pp. 217-224, IEEE International Conference on Social Computing, 2010.
