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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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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