Questing for Justice on Twitter: Topic Modeling of #StopAsianHate Discourses in the Wake of Atlanta Shooting

• Published in: Crime and delinquency Vol. 69; no. 13-14; pp. 2874 - 2900
• Main Authors: Lee, Claire Seungeun, Jang, Ahnlee
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA SAGE Publications 01.12.2023; SAGE PUBLICATIONS, INC
• Subjects: Asian Americans; Data mining; Discourses; Discrimination; Hate crimes; Mass media effects; Racism; Shooting; Shootings; Social justice; Social media; Social networks; Temporal patterns; Time; Topics; Women; Atlanta Georgia; United States > US; Twitter; topic modeling; social media activism; StopAsianHate; anti-Asian hate crime and violence
• ISSN: 0011-1287; 1552-387X
• DOI: 10.1177/00111287211057855

On March 16, 2021, a shooting in Atlanta killed eight people, six were women of Asian descent. This creates a new atmosphere online and offline to discuss hate crimes, racism, and violence against Asian Americans in the United States. The current research utilizes structural topic modeling and text mining to explore how the 2021 Atlanta shooting ignited debates and public discourse on the #StopAsianHate-related conversations on Twitter. The study analyzes the first 7 days of the shooting to explore the temporal patterns and emergent topics of Twitter discourses. Findings show that salient topics and temporal patterns differ from day to day, but topics such as “stand with AAPI community” and “stop racism” are prevalent throughout the 7-day period. This study discusses social media’s role in shaping and reporting public discourses, that is, how digital justice is exercised, and offers social and policy implications. There can be implications for social media’s role in shaping and reporting public discourses on social phenomena with digital justice.