Kwon Soyeon, Park Albert
Department of Management Information System, College of Business, Dongguk University, 30, Pildong-ro 1gil, Jung-gu, Seoul, 04620, Republic of Korea.
Department of Software and Information Systems, College of Computing and Informatics, UNC Charlotte, Woodward 310H, 9201 University City Blvd, Charlotte, NC, 28223, USA.
Comput Human Behav. 2023 Jul;144:107734. doi: 10.1016/j.chb.2023.107734. Epub 2023 Mar 15.
Social media discourse has become a key data source for understanding the public's perception of, and sentiments during a public health crisis. However, given the different niches which platforms occupy in terms of information exchange, reliance on a single platform would provide an incomplete picture of public opinions. Based on the schema theory, this study suggests a 'social media platform schema' to indicate users' different expectations based on previous usages of platform and argues that a platform's distinct characteristics foster distinct platform schema and, in turn, distinct nature of information. We analyzed COVID-19 vaccine side effect-related discussions from Twitter, Reddit, and YouTube, each of which represents a different type of the platform, and found thematic and emotional differences across platforms. Thematic analysis using -means clustering algorithm identified seven clusters in each platform. To computationally group and contrast thematic clusters across platforms, we employed modularity analysis using the Louvain algorithm to determine a semantic network structure based on themes. We also observed differences in emotional contexts across platforms. Theoretical and public health implications are then discussed.
社交媒体话语已成为理解公众在公共卫生危机期间的认知和情绪的关键数据源。然而，鉴于不同平台在信息交流方面占据不同的细分领域，仅依赖单一平台将无法全面呈现公众舆论。基于图式理论，本研究提出一种“社交媒体平台图式”，以表明用户基于先前平台使用情况的不同期望，并认为平台的独特特征会催生独特的平台图式，进而产生独特的信息性质。我们分析了来自推特、红迪网和优兔上与新冠疫苗副作用相关的讨论，每个平台都代表了不同类型的平台，并发现了各平台之间的主题和情感差异。使用K均值聚类算法进行的主题分析在每个平台中识别出七个聚类。为了通过计算对各平台的主题聚类进行分组和对比，我们采用基于鲁汶算法的模块化分析来确定基于主题的语义网络结构。我们还观察到各平台在情感语境上的差异。随后讨论了理论和公共卫生方面的意义。
