Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter.

Abstract

As the COVID‐19 pandemic has unfolded, Hate Speech on social media about China and Chinese people has encouraged social stigmatization. For the historical and humanistic purposes, this history‐in‐the‐making needs to be archived and analyzed. Using the query “china+and+coronavirus” to scrape from the Twitter API, we have obtained 3,457,402 key tweets about China relating to COVID‐19. In this archive, in which about 40% of the tweets are from the U.S., we identify 25,467 Hate Speech occurrences and analyze them according to lexicon‐based emotions and demographics using machine learning and network methods. The results indicate that there are substantial associations between the amount of Hate Speech and demonstrations of sentiments, and state demographics factors. Sentiments of surprise and fear associated with poverty and unemployment rates are prominent. This digital archive and the related analyses are not simply historical, therefore. They play vital roles in raising public awareness and mitigating future crises. Consequently, we regard our research as a pilot study in methods of analysis that might be used by other researchers in various fields.