摘要
【目的】针对医疗舆情事件,识别微博中的意见领袖并研究其影响力。【方法】融合用户个人属性、网络特征、行为特征和文本特征,构建意见领袖识别的综合指标体系,通过聚类分析挖掘医疗舆情事件不同生命周期阶段的意见领袖,并利用时差相关分析研究意见领袖的情感倾向对普通大众情绪的影响。【结果】以2018年疫苗事件为例,验证了本文意见领袖识别模型的有效性。结果表明不同阶段的医疗舆情热点和意见领袖类型均有所不同,并且意见领袖的观点和态度对普通大众的情感具有引导作用。【局限】仅针对疫苗事件进行实证分析,在模型泛化性验证方面有待提高。【结论】本文提出的融合多特征的意见领袖识别方法较传统的评价指标能够更好地发现草根用户中潜在的意见领袖。
[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.
作者
吴江
赵颖慧
高嘉慧
Wu Jiang;Zhao Yinghui;Gao Jiahui(School of Information Management,Wuhan University,Wuhan 430072,China;Center for E-commerce Research and Development,Wuhan University,Wuhan 430072,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2019年第4期53-62,共10页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金面上项目"内容关系互动下的在线医疗社区用户行为演化研究"(项目编号:71573197)的研究成果之一
关键词
医疗舆情
意见领袖
聚类分析
时差相关分析
文本分析
Medical Public Opinion
Opinion Leader
Clustering Analysis
Time Difference Correlation Analysis
Text Analysis
