Analysis and visualization of social user communities

Article Sidebar

PDF

ISSN online: 2255-2863

Prefijo Doi: 10.14201

Main Article Content

Daniel López Sánchez
Discovergy GmbH
Germany
Jorge Revuelta
ACM Member
Fernando De La Prieta
Sunchon National University
Korea, Republic of
Cach Dang
HoChiMinh City University of Transport
Viet Nam
Vol. 4 No. 3 (2015), Articles, pages 11-18
DOI: https://doi.org/10.14201/ADCAIJ2015431118
Accepted: Jun 6, 2016
Copyright How to Cite

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

Downloads

Download data is not yet available.

Article Details

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of statistical mechanics: theory and experiment, 2008(10), P10008.

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.