Integrating User's Emotional Behavior for Community Detection in Social Networks

The analysis of social networks is a very challenging research area. A fundamental aspect concerns the detection of user communities, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Detecting communities is of great importance in sociology, biology as well as computer science where systems are often represented as graphs. In this paper we present a novel methodology for community detection based on users’ emotional behavior. The methodology analyzes user’s tweets in order to determine their emotional behavior in Ekman emotional scale. We define two different metrics to count the influence of produced communities. Moreover, the weighted version of a modularity community detection algorithm is utilized. Our results show that our proposed methodology creates influential enough communities.

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