User interest community detection on social media using collaborative filtering

Abstract

Community detection in microblogging environment has become an important tool to understand the emerging events. Most existing community detection methods only use network topology of users to identify optimal communities. These methods ignore the structural information of the posts and the semantic information of users’ interests. To overcome these challenges, this paper uses User Interest Community Detection model to analyze text streams from microblogging sites for detecting users’ interest communities. We propose HITS Latent Dirichlet Allocation model based on modified Hypertext Induced Topic Search and Latent Dirichlet Allocation to distil emerging interests and high-influence users by reducing negative impact of non-related users and its interests. Moreover, we propose HITS Label Propagation Algorithm method based on Label Propagation Algorithm and Collaborative Filtering to segregate the community interests of users more accurately and efficiently. Our experimental results demonstrate the effectiveness of our model on users’ interest community detection and in addressing the data sparsity problem of the posts.