Recent studies have documented increases in anti-Asian hate throughout the COVID-19 pandemic. Yet relatively little is known about how anti-Asian content on social media, as well as positive messages to combat the hate, have varied over time. In this study, we investigated temporal changes in the frequency of anti-Asian and counter-hate messages on Twitter during the first 16 months of the COVID-19 pandemic. Using the Twitter Data Collection Application Programming Interface, we queried all tweets from January 30, 2020 to April 30, 2021 that contained specific anti-Asian (e.g., #chinavirus, #kungflu) and counter-hate (e.g., #hateisavirus) keywords. From this initial data set, we extracted a random subset of 1,000 Twitter users who had used one or more anti-Asian or counter-hate keywords. For each of these users, we calculated the total number of anti-Asian and counter-hate keywords posted each month. Latent growth curve analysis revealed that the frequency of anti-Asian keywords fluctuated over time in a curvilinear pattern, increasing steadily in the early months and then decreasing in the later months of our data collection. In contrast, the frequency of counter-hate keywords remained low for several months and then increased in a linear manner. Significant between-user variability in both anti-Asian and counter-hate content was observed, highlighting individual differences in the generation of hate and counter-hate messages within our sample. Together, these findings begin to shed light on longitudinal patterns of hate and counter-hate on social media during the COVID-19 pandemic.
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Interpreting Malicious Responses to an Online Questionnaire about Transgender Undergraduate Engineering and Computer Science Student Experiences
Online research that solicits participation from marginalized communities or is conducted by scholars of marginalized identities may be targeted by individuals who intend to tamper with the study outcomes and/or harass the researchers. Our goal is to identify and interpret malicious responses recorded in a first-of-its-kind national questionnaire for transgender and gender nonconforming (TGNC) students in undergraduate engineering and computer science programs. Data categorized as malicious (50 of the 349 total responses) contained slurs, hate speech, or direct targeting of the research team. The data was coded inductively and discursively interpreted through social justice frameworks. The responses contained homophobic, transphobic, ableist, anti-Black, antisemitic, and anti-Indigenous content. Online memes associated with white nationalist and fascist movements were present throughout the data, alongside memes and content referencing gaming and “nerd” culture. Malicious responses can provide critical insight into the social conditions in STEM education. In application, we call for researchers to critically analyze, rather than discard, malicious data to shed light on these phenomena and generate empowering “counterspeech” to confront hate and reclaim agency. These findings show that social justice STEM education must include perspectives on online hate radicalization and center anti-colonial, intersectional solidarity organizing as its opposition.
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- Award ID(s):
- 1764103
- PAR ID:
- 10526830
- Publisher / Repository:
- Northwestern University Libraries
- Date Published:
- Journal Name:
- Bulletin of applied transgender studies
- Volume:
- 2
- Issue:
- 1-2
- ISSN:
- 2769-2124
- Page Range / eLocation ID:
- 67-94
- Subject(s) / Keyword(s):
- transgender STEM computer science hate speech
- Format(s):
- Medium: X
- Right(s):
- Creative Commons Attribution Non Commercial No Derivatives 4.0 International
- Sponsoring Org:
- National Science Foundation
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