Abstract
In this paper, a novel agent-based platform for Twitter user clustering is proposed. We describe how our system tracks the activity for a given topic in the social network and how to detect communities of users with similar political preferences by means of the Louvain Modularity. The quality of this clustering method is evaluated against a subset of human-labeled user profiles. Finally, we propose combining community detection with a force-directed graph algorithm to produce a visual representation of the political communities.
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Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 2008(10), P10008 (2008)
Bostock, M., Ogievetsky, V., Heer, J.: D3 data-driven documents. IEEE Transactions on Visualization and Computer Graphics 17(12), 2301–2309 (2011)
Klusch, M. (ed.): Intelligent information agents: agent-based information discovery and management on the Internet. Springer Science & Business Media (2012)
Liu, B.: Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies 5(1), 1–167 (2012)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: Homophily in social networks. Annual review of sociology, 415–444 (2001)
Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42. ACM, October 2007
Newman, M.E.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103(23), 8577–8582 (2006)
Nguyen, D., Demeester, T., Trieschnigg, D., Hiemstra, D.: 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, October 2012
Pfalzner, S., Gibbon, P: Many-Body Tree Methods in Physics. Cambridge University Press (2005)
Schrenk, M.: Webbots, Spiders, and Screen Scrapers: A Guide to Developing Internet Agents with PHP/CURL. No Starch Press (2012)
Stefanidis, A., Crooks, A., Radzikowski, J.: Harvesting ambient geospatial information from social media feeds. GeoJournal 78(2), 319–338 (2013)
Tapscott, D.: Grown Up Digital: How the Net Generation is Changing Your World HC. McGraw-Hill (2008)
Westerman, D., Spence, P.R., Van Der Heide, B.: Social media as information source: Recency of updates and credibility of information. Journal of Computer-Mediated Communication 19(2), 171–183 (2014)
Sanchez Martin, A.J., de la Prieta Pintado, F., De Gasperis, G.:Fixing and evaluating texts: mixed text reconstruction method for data fusion environments. In: 2014 17th International Conference on Information Fusion (FUSION), pp. 1–6. IEEE, July 2014
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Sánchez, D.L., Revuelta, J., De la Prieta, F., Gil-González, A.B., Dang, C. (2016). Twitter User Clustering Based on Their Preferences and the Louvain Algorithm. In: de la Prieta, F., et al. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_29
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DOI: https://doi.org/10.1007/978-3-319-40159-1_29
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