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

Lee, Claire Seungeun; Jang, Ahnlee

doi:10.1177/00111287211057855

Cited by 18 publications

(11 citation statements)

References 48 publications

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“…Expanding our scope from Asians in general to anti-Asian hate-related questions, we observed questions and answers on new topics not discussed before the pandemic in Quora, including the need for an Asian solidarity movement as it appeared on another social media platform ( Jun et al, 2021 ), such as Twitter ( Lee & Jang, 2021 ). Finally, while the semantic similarity between Asian-Black remained stable, the semantic similarity between Asian-White and Asian-Hispanic diverged after COVID-19.…”

Section: Discussionmentioning

confidence: 99%

Anti-Asian Discourse in Quora: Comparison of Before and During the COVID-19 Pandemic with Machine- and Deep-Learning Approaches

Jang

Youm

2022

Race and Justice

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The current study attempts to compare anti-Asian discourse before and during the COVID-19 pandemic by analyzing big data on Quora, one of the most frequently used community-driven knowledge sites. We created two datasets regarding “Asians” and “anti-Asians” from Quora questions and answers between 2010 and 2021. A total of 1,477 questions and 5,346 answers were analyzed, and the datasets were divided into two time periods: before and during the COVID-19 pandemic. We conducted machine-learning-based topic modeling and deep-learning-based word embedding (Word2Vec). Before the pandemic, the topics of physical difference and racism were prevalent, whereas, after the pandemic, the topics of hate crime, the need to stop Asian hate crimes, and the need for the Asian solidarity movement emerged. Above all, the semantic similarity between Asian and Black people became closer, while the similarity between Asian people and other racial/ethnic groups was diminished. The emergence of negative and radical language, which increased saliently after the outbreak of the pandemic, and the considerably wider semantic distance between Asian and White people indicates that the relationship between the two races has been weakened. The findings suggest a long-term campaign or education system to reduce racial tensions during the pandemic.

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Section: Discussionmentioning

confidence: 99%

Anti-Asian Discourse in Quora: Comparison of Before and During the COVID-19 Pandemic with Machine- and Deep-Learning Approaches

Jang

Youm

2022

Race and Justice

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“…Twitter is free and public open network in digital communication (Lee & Jang, 2021), making it a useful large-scale data source to understand people's opinions, social interactions, and mobilization. Terrorist groups, particularly ISIS, utilizes Twitter to disseminate propaganda, justify actions, recruit, threaten, as well as convey support and spread fear (Choi et al, 2018).…”

Section: Data Collectionmentioning

confidence: 99%

ISIS’s COVID-19 Messaging on Twitter: An Analysis of Tweet Sentiment and Emotions

Lee

Colautti

2022

Crime & Delinquency

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Terrorist groups have integrated social discourse into their narratives to exploit public fear. The COVID-19 pandemic has provided an opportunity to measure how ISIS modified narratives, stimulated group communication, reinforced enemy hatred, and employed new mobilization/recruitment strategies. Using Significance Quest Theory, this study examines ISIS’s Twitter activity related to COVID-19 ( N = 630,201) and how the group leverages the psychological effects and the social disruption. This study employs a two-stage analysis of tweet sentiment and emotion with a statistical analysis of retweet-motivating factors to explore how ISIS manages its narrative, needs, and network. Results show content with higher negative sentiment scores had more retweets across all sample sets. Insights include deeper understanding of extremist activity and group adaptation to external changes.

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“…Social media, particularly Twitter, is an effective communication tool for political communication and mobilization (Gerbaudo, 2012 ; Enli and Skogerbo, 2013 ; Sandoval-Almazan and Gil-Garcia, 2013 ; Theocharis et al, 2015 ; Lee and Jang, 2021 ). In the age of Trump, Twitter emerged as his preferred platform to effectively communicate with his supporters and political opponents and convey diplomatic agendas (Ott, 2017 ; Stolee and Caton, 2018 ; Simunjak and Caliandro, 2019 ; Nacos et al, 2021 ).…”

Section: Current Studymentioning

confidence: 99%

Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection

et al. 2022

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The transfer of power stemming from the 2020 presidential election occurred during an unprecedented period in United States history. Uncertainty from the COVID-19 pandemic, ongoing societal tensions, and a fragile economy increased societal polarization, exacerbated by the outgoing president's offline rhetoric. As a result, online groups such as QAnon engaged in extra political participation beyond the traditional platforms. This research explores the link between offline political speech and online extra-representational participation by examining Twitter within the context of the January 6 insurrection. Using a mixed-methods approach of quantitative and qualitative thematic analyses, the study combines offline speech information with Twitter data during key speech addresses leading up to the date of the insurrection; exploring the link between Trump's offline speeches and QAnon's hashtags across a 3-day timeframe. We find that links between online extra-representational participation and offline political speech exist. This research illuminates this phenomenon and offers policy implications for the role of online messaging as a tool of political mobilization.

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

Cited by 18 publications

References 48 publications

Anti-Asian Discourse in Quora: Comparison of Before and During the COVID-19 Pandemic with Machine- and Deep-Learning Approaches

Anti-Asian Discourse in Quora: Comparison of Before and During the COVID-19 Pandemic with Machine- and Deep-Learning Approaches

ISIS’s COVID-19 Messaging on Twitter: An Analysis of Tweet Sentiment and Emotions

Storm the Capitol: Linking Offline Political Speech and Online Twitter Extra-Representational Participation on QAnon and the January 6 Insurrection

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