From #BlackLivesMatter to #StopAsianHate: Examining network agenda-setting effects of hashtag activism on Twitter

Abstract

With large, representative, and comparable data scraped from Twitter, this study tries to provide comprehensive understanding of the salient topics under #BlackLivesMatter and #StopAsianHate online movements. Employing semi-supervised Latent Dirichlet allocation topic modeling, five topics have been extracted from 3-month tweets data after George Floyd’s death in 2020. Six topics have been extracted from 3-month tweets data after the Atlantic spa shooting tragedy in 2021. Both movements reflected salient topics on the tragedy that just took place during the data collection period. In addition, general violence, collective actions, community support, and criticism on White racism are all identified as important issues of the counter-racism discourse flooded on social media. In addition, our study explores the network agenda-setting effects of hashtag activisms. The results show that issue networks of the first 2 weeks’ counter-racism discourses after the crime could not set network agenda for the next 2 weeks’ discourses. However, the network agenda-setting effects became significant after the first 2 weeks and stayed stable as time went on. In addition, we do not find a significant relationship between issue networks of the two movements under study. It counter-argues any assumption that one counter-racism movement online could trigger similar movements among different groups.