Abstract
In this paper, we try to cluster twitter users into different communities. These communities can be overlapping based on their interests. The paper proposed a RWC (relation-weight-clustering) model to construct twitter users’ network. This model takes twitter users’ “@” and “RT@” behaviors into account. By counting their “@” and “RT@” frequency, the relation strength can be then descripted. Using SVM, we can get the users interest vector by analyzing their tweets. And the common interest vector between two users is calculated according to their common interests. Using community detection algorithm to resolve the relation-nodes-based network, the overlapping communities are formed with modularity of 0.682.
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Guo, L., Ding, Z., Wang, H. (2016). Behavior-Based Twitter Overlapping Community Detection. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_31
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DOI: https://doi.org/10.1007/978-3-319-32055-7_31
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