Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network
Wang Xiwei1,2,3,Jia Ruonan1(),Wei Yanan1,Zhang Liu1
1School of Management, Jilin University, Changchun 130022, China 2Research Center for Big Data Management, Jilin University, Changchun 130022, China 3Cyberspace Governance Research Center, Jilin University, Changchun 130022, China
[Objective] User groups are the main units to disseminate public opinion. This study identifies the characteristics of user groups through clustering techniques, which could help social network companies provide better services. [Methods] With the help of Group Theory, we clustered users based on their influence, sentiments, and behaviors. First, we collected user data from the Sina Weibo. Then, we utilized Canopy and K-Means algorithms to cluster users. Finally, we visualized our findings with Neo4j and Weka. [Results] User groups of the same public opinion event were different in emotion, influence, and behaviors, while user groups from different public opinion events shared common characteristics. [Limitations] Both public opinion events in this study happened at Chinese universities, and we only collected data from Sina Weibo. [Conclusions] Based on the clustering results, we could propose effective administration strategies for each user group in the same or different public opinion events.
王晰巍,贾若男,韦雅楠,张柳. 多维度社交网络舆情用户群体聚类分析方法研究*[J]. 数据分析与知识发现, 2021, 5(6): 25-35.
Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network. Data Analysis and Knowledge Discovery, 2021, 5(6): 25-35.
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