Semantically meaningful group detection within sub-communities of Twitter blogosphere: a topic oriented multi-objective clustering approach

Authors:
Dionisios N. Sotiropoulos;Chris D. Kounavis;George M. Giaglis
Affiliations:
Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece;Athens University of Economics and Business, Athens, Greece
Venue:
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Year:
2013

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Abstract

This paper addresses the problem of semantically meaningful group detection within a sub-community of twitter micro-bloggers by utilizing a topic modeling, multi-objective clustering approach. The proposed group detection method is anchored on the Latent Dirichlet Allocation (LDA) topic modeling technique, aiming at identifying clusters of twitter users that are optimal in terms of both spatial and topical compactness. Specifically, the group detection problem is formulated as a multi-objective optimization problem taking into consideration two complementary cluster formation directives. The first objective, related to spatial compactness, is achieved by minimizing the overall deviation from the corresponding cluster centers. The second, related to topical compactness, is achieved by minimizing the portion of probability mass assigned to low probability topics for the corresponding cluster centroids. In our approach, optimization is performed by employing a multi-objective genetic algorithm, which results in a variety of cluster structures that are significantly more interpretable than cluster assignments obtained with traditional single-objective clustering algorithms.