Event-based Community Detection in Micro-Blog Networks

doi:10.23940/ijpe.21.01.p6.6073

Abstract

Abstract:

With the rapid development of mobile Internet, micro-blogs have been already integrated into people's work and family life. Traditional topic-based semantic community detection methods have not considered the sentiment tendency of users towards specific topics and therefore increase the probability of community fragmentation. To solve this problem, we propose an event-based community detection framework in the micro-blog network. Firstly, the major events in micro-blog within a sliding window are extracted. Then, the user's sentiment polarity of each event is used as the initial community label of each individual. Finally, the community detection procedure can be implemented by iteratively updating the community label. The simulation results indicate that the proposed method can capture the consistency of views of members within the same community, which brings higher accuracy and sentiment cohesion.

Key words: micro-blog networks, community detection, event extraction, sentiment tendency, label propagation

Hailu Yang, Ying Zhang, Jin Zhang, Deyun Chen, and Guanglu Sun. Event-based Community Detection in Micro-Blog Networks [J]. Int J Performability Eng, 2021, 17(1): 60-73.

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