Characterizing the dissemination of misinformation on social media in health emergencies: An empirical study based on COVID-19

The dissemination of misinformation in health emergencies poses serious threats to public health and increases health anxiety. To understand the underlying mechanism of the dissemination of misinformation regarding health emergencies, this study creatively draws on social support theory and text mining. It also explores the roles of different types of misinformation, including health advice and caution misinformation and health help-seeking misinformation, and emotional support in affecting individuals’ misinformation dissemination behavior on social media and whether such relationships are contingent on misinformation ambiguity and richness. The theoretical model is tested using 12,101 textual data about COVID-19 collected from Sina Weibo, a leading social media platform in China. The empirical results show that health caution and advice, help seeking misinformation, and emotional support significantly increase the dissemination of misinformation. Furthermore, when the level of ambiguity and richness regarding misinformation is high, the effect of health caution and advice misinformation is strengthened, whereas the effect of health help-seeking misinformation and emotional support is weakened, indicating both dark and bright misinformation ambiguity and richness. This study contributes to the literature on misinformation dissemination behavior on social media during health emergencies and social support theory and provides implications for practice.