Abstract:

Social media plays a particularly important role during the COVID-19 outbreak as an important channel for information transfer, opinion exchange, and help-seeking. Therefore, the promotion of knowledge sharing among users is a hot topic in social media research. Taking the COVID-19 pandemic as the research background, this paper obtains problem characteristics by topic clustering based on the LDA algorithm and investigates the effects of problem characteristics and user characteristics on knowledge sharing from problem informativeness and problem focusing degree. Moreover, it also considers the three stages of the event development. The results show that first knowledge sharing behavior can be effectively promoted by increasing the informativeness and focusing degree of problems. In different stages of event development (initial stage, outbreak stage, and mitigation stage), the impact of problem characteristics and user characteristics is heterogeneous. The moderating effect of social reputation on the relationship between identity/connection and knowledge sharing behavior is significant. This paper provides a new perspective for the research on knowledge sharing behavior, and the research results have a certain guiding significance for user behavior and platform rule-making.

Key words:

knowledge sharing, social media, problem informativeness, problem focusing degree, user characteristic

摘要：

社交媒体作为信息传递、观点交流、帮助寻求的重要媒介，在新冠肺炎病毒爆发期间发挥着尤为重要的作用。如何促进用户知识共享是社交媒体研究热点。以新冠肺炎疫情为研究背景，从谁发帖子和如何发帖两个维度探究在线知识共享行为。首先，基于LDA算法对相关帖子进行主题聚类，获得帖子的问题特征；纳入问题特征与用户特征，重点关注帖子问题信息量与聚焦度，构建用户知识共享行为的影响因素模型；考虑事件发展的3个阶段，探究影响因素在不同阶段影响的差异性。研究结果表明：通过提高问题聚焦度与信息量可以有效促进用户的在线知识共享行为。在事件发展的不同阶段（初期、爆发期、缓和期），问题特征和用户特征的影响具有异质性。社会认同对身份标识/社会连接与知识共享行为关系的调节作用显著。为知识共享行为的相关研究提供了新的研究视角，研究结果对用户行为和平台规则制定具有一定的指导作用。

关键词:

知识共享, 社交媒体, 问题信息量, 问题聚焦度, 用户特征