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The COVID-19 pandemic was accompanied by an infodemic, which has now become a global concern. Despite the relatively timely and extensive guidelines regarding COVID-19 prevention and treatment, effective and standardized solutions for managing this infodemic are still lacking. In light of the ubiquity of social media in China, various algorithms have been applied to new media platforms to help combat COVID-19, particularly, misinformation and disinformation. Inspired by the model of ‘blocking the spread of the virus, treating the infected population, and improving immunity’ for the prevention and control of the COVID-19 pandemic, this study examines three dominant forms of algorithms—collaborative filtering recommendation, content-based recommendation, and knowledge-based recommendation—and proposes a theoretical model called Block, Push, and Intervene (BPI). This model calls for the timely blocking of misinformation and disinformation, precisely delivering authentic information to people affected by the infodemic and intervening in some potential issues in advance. Based on the BPI framework, we conducted semi-structured interviews with relevant staffs in charge of Bytedance, Tencent, Sina Weibo, Baidu, and The National Internet Information Office's Center for Reporting Illegal and Adverse Information, to summarize the patterns of algorithms used against the infodemic. Additionally, an online panel survey is used to analyze public perceptions of the severity of the infodemic on each platform. By evaluating the cross-validated results of the survey sample and semi-structured interviews on the role of algorithms against infodemic, this study contributes both to the understanding of the working details and practices surrounding information epidemics in the context of China, as well as to the systematic research on the unique use of information technology in the midst of public health crises.

An Empirical Research on How to Tackle Infodemic in China: Stakeholders and Algorithms