Anti-Asian Discourse in Quora: Comparison of Before and During the COVID-19 Pandemic with Machine- and Deep-Learning Approaches

Abstract

The current study attempts to compare anti-Asian discourse before and during the COVID-19 pandemic by analyzing big data on Quora, one of the most frequently used community-driven knowledge sites. We created two datasets regarding "Asians" and "anti-Asians" from Quora questions and answers between 2010 and 2021. A total of 1,477 questions and 5,346 answers were analyzed, and the datasets were divided into two time periods: before and during the COVID-I9 pandemic. We conducted machine-learning-based topic modeling and deep-learning-based word embedding (VVord2Vec). Before the pandemic, the topics of physical difference and racism were prevalent, whereas, after the pandemic, the topics of hate crime, the need to stop Asian hate crimes, and the need for the Asian solidarity movement emerged. Above all, the semantic similarity between Asian and Black people became closer, while the similarity between Asian people and other racial/ethnic groups was diminished. The emergence of negative and radical language, which increased saliently after the outbreak of the pandemic, and the considerably wider semantic distance between Asian and White people indicates that the relationship between the two races has been weakened. The findings suggest a long-term campaign or education system to reduce racial tensions during the pandemic.