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| 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 |
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Abstract [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.
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Received: 03 February 2020
Published: 06 July 2021
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| Fund:Special Research Project of National Development and Security (Biosafety) of Jilin University(2020JDGFAZ003);Jilin University Postgraduate Innovation Fund(101832020CX057) |
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Corresponding Authors:
Jia Ruonan
E-mail: 2943442131@qq.com
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