Luo Han, Meng Xiao, Zhao Yifei, Cai Meng
School of Humanities and Social Sciences, Xi'an Jiaotong University, Xi'an, 710049, China.
School of Journalism and New Media, Xi'an Jiaotong University, Xi'an, 710049, China.
Comput Human Behav. 2023 Jul;144:107733. doi: 10.1016/j.chb.2023.107733. Epub 2023 Mar 8.
The outbreak of information epidemic in crisis events, with the channel effect of social media, has brought severe challenges to global public health. Combining information, users and environment, understanding how emotional information spreads on social media plays a vital role in public opinion governance and affective comfort, preventing mass incidents and stabilizing the network order. Therefore, from the perspective of the information ecology and elaboration likelihood model (ELM), this study conducted a comparative analysis based on two large-scale datasets related to COVID-19 to explore the influence mechanism of sentiment on the forwarding volume, spreading depth and network influence of information dissemination. Based on machine learning and social network methods, topics, sentiments, and network variables are extracted from large-scale text data, and the dissemination characteristics and evolution rules of online public opinions in crisis events are further analyzed. The results show that negative sentiment positively affects the volume, depth, and influence compared with positive sentiment. In addition, information characteristics such as richness, authority, and topic influence moderate the relationship between sentiment and information dissemination. Therefore, the research can build a more comprehensive connection between the emotional reaction of network users and information dissemination and analyze the internal characteristics and evolution trend of online public opinion. Then it can help sentiment management and information release strategy when emergencies occur.
危机事件中信息疫情的爆发，借助社交媒体的渠道效应，给全球公共卫生带来了严峻挑战。综合信息、用户和环境因素，了解情感信息在社交媒体上的传播方式，对于舆论治理和情感安抚、预防群体事件以及稳定网络秩序起着至关重要的作用。因此，本研究从信息生态学和精细加工可能性模型（ELM）的视角出发，基于两个与新冠疫情相关的大规模数据集进行了对比分析，以探究情感对信息传播的转发量、传播深度和网络影响力的影响机制。基于机器学习和社会网络方法，从大规模文本数据中提取主题、情感和网络变量，进一步分析危机事件中网络舆论的传播特征和演变规律。结果表明，与积极情感相比，消极情感对传播量、传播深度和影响力具有正向影响。此外，信息的丰富性、权威性和话题影响力等特征会调节情感与信息传播之间的关系。因此，该研究能够在网络用户的情感反应与信息传播之间建立更全面的联系，分析网络舆论的内在特征和演变趋势。进而在突发事件发生时有助于进行情感管理和信息发布策略制定。
