Table_1_A comparative analysis of the COVID-19 Infodemic in English and Chinese: insights from social media textual data.docx

The COVID-19 infodemic, characterized by the rapid spread of misinformation and unverified claims related to the pandemic, presents a significant challenge. This paper presents a comparative analysis of the COVID-19 infodemic in the English and Chinese languages, utilizing textual data extracted from social media platforms. To ensure a balanced representation, two infodemic datasets were created by augmenting previously collected social media textual data. Through word frequency analysis, the 30 most frequently occurring infodemic words are identified, shedding light on prevalent discussions surrounding the infodemic. Moreover, topic clustering analysis uncovers thematic structures and provides a deeper understanding of primary topics within each language context. Additionally, sentiment analysis enables comprehension of the emotional tone associated with COVID-19 information on social media platforms in English and Chinese. This research contributes to a better understanding of the COVID-19 infodemic phenomenon and can guide the development of strategies to combat misinformation during public health crises across different languages.

新冠疫情信息疫情（COVID-19 infodemic）以与新冠疫情相关的虚假信息及未证实言论的快速传播为典型特征，带来了严峻挑战。本文针对英语与汉语语境下的新冠疫情信息疫情展开对比分析，所用文本数据均提取自社交媒体平台。为确保样本代表性均衡，研究团队通过扩充此前采集的社交媒体文本数据，构建了两组信息疫情数据集。通过词频分析，研究识别出出现频次最高的30个信息疫情相关词汇，以此揭示信息疫情相关的主流讨论议题。此外，主题聚类分析揭示了各语言语境下的主题结构，加深了对核心话题的理解。同时，情感分析帮助研究者理解英语与汉语社交媒体平台上新冠相关信息所承载的情感基调。本研究有助于深化对新冠疫情信息疫情现象的认知，同时可为跨语言公共卫生危机中打击虚假信息的策略制定提供参考。