#StopAsianHate: Archiving and Analyzing Twitter Discourse in the Wake of the 2021 Atlanta Spa Shootings

Abstract

On March 16, 2021, six Asian women were killed in Atlanta, US, possibly out of racist motivations. This tragic event, now known as the 2021 Atlanta Spa Shootings, precipitated a massive increase in the volume of counter-anti-Asian declarations and discussion on social media platforms such as Twitter. In a pilot study to chronicle and profile public opinions, social movements and patterns in the global Twitter discourse we scraped the Twitter API using the query term "StopAsianHate", obtaining more than 5.5 million tweets and their metadata. By using social movement analytical frameworks to analyze traffic peaks and the use of hashtags, we identified a set of more than 300 frequently used hashtags that can serve as specific query words in future archival ingest activities, as well as the dimensions of and current problems with this social movement. This suggests the utility of this approach for both archiving applications and social-political analyses of emerging topics and concerns.

“We Are Bulletproof”: The Transcultural Power of Fandom in #StopAsianHate

“…Building off Lopez's (2016) study of Asian American media activists, this paper examines how racial justice activism unfolds online. Existing studies on these particular hashtags, coming largely from the field of computer science, focus on finite time periods during the pandemic, from the immediate weeks following the Atlanta-area shootings (Fan, Huizi, and Gilliland 2021;Lee and Jang 2021;Lyu et al 2023) to the weeks following US President Biden's signing of the COVID-19 Hate Crimes Act (Cao et al 2022). Further, researchers have conducted longitudinal examinations of how counter-hate speech, including these hashtags, interacts with anti-Asian hate speech online (He et al 2020;Lin et al 2022).…”

“…Counts for languages are summarized in Table 2. While some previous papers that analyze #StopAsianHate and #StopAAPIHate focus on English tweets only (Cao et al 2022;Fan, Huizi, and Gilliland 2021;Lyu et al 2023), we purposefully include tweets in other languages within our full dataset in order to examine the effects of the hashtags on a global scale. This is particularly relevant due to the language barriers that motivate specific forms of anti-Asian discrimination and racism-further, as the COVID-19 pandemic has revealed, the perception of race often lacks nuance when it comes to anti-Asian racism, with many non-Chinese background being broadly classified as Chinese when encountering blame for the pandemic.…”

Using Machine Learning to Enhance Archival Processing of Social Media Archives

“…While some degree of human input into description is likely to remain desirable [22,41], particularly for providing historical and cultural context, the overall descriptive effort could be ameliorated if certain aspects of those collections could be described automatically and if additional information about the collection as a whole that is not readily discernible to the human eye could be identified.…”

“…Nevertheless, while it is not subject to the same policy rules and evidentiary scrutiny as bureaucratic records, the trustworthiness and transparency of archival administration of social media content may be a matter of public, and sometimes political concern, especially when it is used as a major organ of government communication [7]. Although many archives and libraries preserve social media content [22], there is a pressing need for more research and testing of computational descriptive methods that can not only eliminate some manual descriptive processes, but also surface new knowledge and increase contextualization. As early as 1992, a statistical content analysis demonstrated that a statistical approach could assist in archival description of the content of early social media implementations, and especially in surfacing latent patterns in that content [28].…”

The #StopAsianHate Movement on Twitter: A Qualitative Descriptive Study

“…These contributions provide a rich source of data on this sensitive topic and allow researchers to track, capture, and analyze tweeters’ responses and activities [ 13 ]. In particular, digital data mining of tweets has been used to help identify hot topics and blind spots within the field of racial studies, including in AA Studies [ 16 , 17 ]. Considering the aforementioned features, Twitter is the most appropriate platform to understand public disclosure toward the #StopAsianHate movement.…”