ABSTRACT
This chapter provides an operational definition of misinformation and relates it to the current research on the infodemic. To identify and trace the emergence of debunked yet widely circulating misinformation surrounding the COVID-19 pandemic, we used the computational method to analyze a set of big corpus from Sina Weibo, including temporal-spatial and topical patterns, sources, and message characteristics. Findings show that falsehoods about COVID-19 started with a burst in the early stage of the pandemic and were followed by several peaks of rapid increase. This pattern repeated periodically. The origins of COVID-19 were the most prevalent topic, followed by information publicity and compliance measures. In terms of sources, ordinary individual and male weibo users accounted for most of the posted misinformation, whereas institutional and female users were more likely to publish debunking information. Finally, misinformation appeared to be shorter, with no URLs, and more attractive for wider circulation, making it a challenge to flatten the curve.
