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Data Analysis and Knowledge Discovery  2019, Vol. 3 Issue (4): 53-62    DOI: 10.11925/infotech.2096-3467.2018.1069
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Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents
Jiang Wu1,2(),Yinghui Zhao1,Jiahui Gao1
1School of Information Management, Wuhan University, Wuhan 430072, China
2Center for E-commerce Research and Development, Wuhan University, Wuhan 430072, China
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[Objective] This paper aims to identify Weibo opinion leaders and study their influence in medical public opinion incidents. [Methods] This article integrates user personal attributes, network characteristics, behavioral characteristics and text features to construct a comprehensive index system to identify opinion leaders in different periods of medical public opinion incidents, and also use time difference correlation analysis to study the impact of the emotional tendency of opinion leaders on the public sentiment. [Results] Taking the 2018 vaccine event as a case, this paper verifies the effectiveness of the proposed opinion leader identification model. The results show that the medical public opinion hotspots and the types of opinion leaders differ in different periods, and the attitudes of opinion leaders have a guiding effect on the emotions of the general public. [Limitations] We only examined the performance on the proposed methods with the vaccine event data and the model generalization ability remains underdeveloped. [Conclusions] The multi-feature opinion leader identification method proposed in this paper can better discover potential opinion leaders among grassroots users compared with traditional evaluation indicators.

Key wordsMedical Public Opinion      Opinion Leader      Clustering Analysis      Time Difference Correlation Analysis      Text Analysis     
Received: 26 September 2018      Published: 29 May 2019

Cite this article:

Jiang Wu,Yinghui Zhao,Jiahui Gao. Research on Weibo Opinion Leaders Identification and Analysis in Medical Public Opinion Incidents. Data Analysis and Knowledge Discovery, 2019, 3(4): 53-62.

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