Analysis and visualization of social user communities
Bilah Samping Artikel
ISSN online:
2255-2863
Prefijo Doi:
10.14201
Isi Artikel Utama
Vol. 4 No. 3 (2015), Articles, pages 11-18
Abstract
In this paper, a novel framework for social user clustering is proposed. Given a current controversial political topic, the Louvain Modularity algorithm is used to detect communities of users sharing the same political preferences. The political alignment of a set of users is labeled manually by a human expert and then the quality of the community detection is evaluated against this gold standard. In the last section, we propose a novel force-directed graph algorithm to generate a visual representation of the detected communities.
Keywords:
Data Mining, Information Visualization, Social Communities
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References
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Bostock, M., Ogievetsky, V., & Heer, J. (2011). D³ data-driven documents.Visualization and Computer Graphics, IEEE Transactions on, 17(12), 2301-2309.
Klusch, M. (Ed.). (2012). Intelligent information agents: agent-based information discovery and management on the Internet. Springer Science & Business Media.
Liu, B. (2012). Sentiment Analysis and Opinion Mining: Synthesis Lectures on Human Language Technologies, vol. 16. Morgan & Claypool Publishers, San Rafael.
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociol-ogy, 415-444.
Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007, October). Measurement and analysis of online social networks. InProceedings of the 7th ACM SIGCOMM conference on Internet measurement(pp. 29-42). ACM.
Newman, M. E. (2006). Modularity and community structure in networks.Proceedings of the national academy of sciences, 103(23), 8577-8582.
Nguyen, D., Demeester, T., Trieschnigg, D., & Hiemstra, D. (2012, October). Federated search in the wild: the combined power of over a hundred search engines. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 1874-1878). ACM.
Pfalzner, S., & Gibbon, P. (2005). Many-body tree methods in physics. Cambridge University Press.
Schrenk, M. (2012). Webbots, spiders, and screen scrapers: A guide to developing Internet agents with PHP/CURL. No Starch Press.
Stefanidis, A., Crooks, A., & Radzikowski, J. (2013). Harvesting ambient geospatial information from social media feeds. GeoJournal, 78(2), 319-338.
Tapscott, D. (2008). Grown Up Digital: How the Net Generation is Changing Your World HC. McGraw-Hill.
Westerman, D., Spence, P. R., & Van Der Heide, B. (2014). Social media as information source: Recency of updates and credibility of information. Journal of Computer?Mediated Communication, 19(2), 171-183.