Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter

Yuze Sha, Nicholas Micallef, Yan Wu

Abstract: This paper takes a multidisciplinary approach and conducts a three-dimensional analysis of #StopAsianHate tweets from January 1, 2021, to December 31, 2022 by combining computer science, applied linguistics, and cultural studies. It employs a ‘funnel approach’, from a broad examination to specific sentimental and linguistic dimensions within the top 10% most engaged tweets. The analysis reveals that the #StopAsianHate hashtag is primarily used for counter-discourse against Anti-Asian hate crime, expressing collective in-group identity and inclusionary out-group solidarity against racism. A key finding is the representation of Asian people as the ‘model minority’, derived from combined analyses of sentiments, politeness, toxicity, and Corpus-Assisted Critical Discourse Analysis of the tweets. The #StopAsianHate movement is characterised as moderate, evidenced by the large number of tweets with positive sentiment scores and frequent relational identification, which refers to anti-racism supporters as ‘friends’, ‘folks’, and ‘family’. Though negative sentiment scores are also prevalent, they are found non-toxic and can be explained by tweet genre’s rare use of polite expressions, as well as the prominence of #StopAsianHate thematic words such as ‘hate’, ‘racism’, and ‘crime’, serving as tools to challenge racism. Most notably, the study provides fresh insights into the growing self-reflective collective awareness of the negative impacts of ‘model minority’ stereotypes within the Asian communities and discusses ongoing opportunities and challenges in #StopAsianHate movement.