基于社交网络文本挖掘的上市公司负面传闻扩散机理研究

摘要： 识别传闻扩散的关键要素、勾勒其交织演变特征并剖析扩散机理，有助上市公司制定有效的舆论危机管理策略以减少损失。本研究首先分析传闻扩散路径识别社交网络中负面传闻扩散的关键要素，然后以“海天双标”事件为例，爬取新浪微博数据，基于生命周期理论以及社交网络分析、LDA主题模型等方法，运用Gephi和Python挖掘得出关键要素的量值并分析扩散网络演变特征，最后依据扩散机理提出行之有效的危机管理策略。研究发现，社交网络负面传闻扩散的用户、信息内容、时段三个关键要素在扩散中交织演变，用户度中心性随生命周期演进呈倒U型，关键用户主体从草根的未认证用户转向较为权威的认证用户、最后又回归未认证用户，内容主题逐步深入至本质问题后转向长期表现，成长期是事态演变的关键时段，上市公司可在此时段聚焦关键用户与内容主题并积极应对。

关键词: 信息扩散, 负面传闻, 社交网络分析, 文本挖掘, 网络舆情

Abstract: This study investigates the key factors driving rumor diffusion, traces their intertwined evolution, and analyzes the diffusion mechanisms to support listed companies in formulating effective crisis management strategies to mitigate potential losses. First, this study examines diffusion paths to identify key elements in the spread of negative rumors on social networks. Using the "Haitian double standards" incident as a case study, this research collects data from Sina Weibo and analyzes it within a life-cycle theoretical framework, employing methods such as social network analysis and LDA topic modeling. Tools like Gephi and Python are further utilized to identify key elements and trace the evolution of the diffusion network. Finally, practical crisis management strategies are proposed based on the diffusion mechanism. This study found that the three key elements in the spread of negative rumors on social networks, namely users, information content and time periods, are intertwined and evolving during the spreading process. As time progresses, user degree centrality shows an inverted U-shape. Key participants transition from grassroots, non-verified users to authoritative, verified users, and eventually back to non-verified users. Meanwhile, content themes evolve towards core issues and then transition to long-term performance, with the growth phase being a crucial period for the progression of the event. Listed companies can enhance their crisis response by targeting influential users and addressing critical content themes during this phase to proactively mitigate risks.

Key words: Information diffusion, Negative rumors, Social network analysis, Text mining, Internet public opinion

中图分类号: 

G206

网址：基于社交网络文本挖掘的上市公司负面传闻扩散机理研究 https://m.mxgxt.com/news/view/2017356

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