Detecting micro-blog user interest communities through the integration of explicit user relationship and implicit topic relations

• Published in: Science China. Information sciences Vol. 60; no. 9; pp. 60 - 73
• Main Authors: Qin, Yu, Yu, Zhengtao, Wang, Yanbing, Gao, Shengxiang, Shi, Linbin
• Format: Journal Article
• Language: English
• Published: Beijing Science China Press 01.09.2017; Springer Nature B.V
• Subjects: Algorithms; Computer Science; Dirichlet problem; Information Systems and Communication Service; Microblogs; Research Paper; Social networks; Tagging; User behavior; 关系网络; 显式; 检测; 用户兴趣; 用户行为; 社区; 隐含; 集成; 特征映射; 兴趣社区; 有指导LDA; 邻居节点贡献度; feature mapping; 隐式主题; guided topic model; implicit topic; contribution of the neighboring nodes
• ISSN: 1674-733X; 1869-1919
• DOI: 10.1007/s11432-015-0899-6

In order to effectively utilize the explicit user relationship and implicit topic relations for the detec- tion of micro-blog user interest communities, a micro-blog user interest community （MUIC） detection approach is proposed. First, through the analysis of the follow relationship between users, we have defined three types of such relationships to construct the user follow-ship network. Second, taking the semantic correlation between user tags into account, we construct the user interest feature vectors based on the concept of feature mapping to build a user tag based interest relationship network. Third, user behaviors, such as reposting, comment- ing, replying, and receiving comments from others, are able to provide certain guidance for the extraction of micro-blog topics. Hence, we propose to integrate the four mentioned user behaviors that are considered to provide guidance information for the traditional latent Dirichlet allocation （LDA） model. Thereby, in addition to the construction of a topic-based interest relationship network, a guided topic model can be built to extract the topics in which the user is interested. Finally, with the integration of the afore-mentioned three types of relationship network, a micro-blog user interest relationship network can be created. Meanwhile, we propose a MUIC detection algorithm based on the contribution of the neighboring nodes. The experiment result proves the effectiveness of our approach in detecting MUICs.