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#StopAsianHate Counterspeech on Twitter: Effectiveness of Counterspeech Strategies and Geospatial Analysis

Kabir, Md Enamul
http://orcid.org/0000-0003-3810-4000
http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169965912014087

Abstract Details

2023, Doctor of Philosophy (Ph.D.), Bowling Green State University, Media and Communication.
This dissertation investigates the effectiveness of counterspeech strategies employed on Twitter in response to anti-Asian hate during the COVID-19 pandemic. This research delves into the communicative strategies, emotional tones, and geospatial distribution of counterspeech, specifically focusing on its effectiveness in the United States. A supervised machine learning was employed to classify counterspeech tweets and counterspeech strategies based on empirical typology. By analyzing 106,388 tweets associated with the hashtag #StopAsianHate collected from November 2021 to May 2022, this research provides insights into the varied effectiveness of counterspeech strategies. The analysis revealed that though counterspeakers were using more negative tones in counterspeech tweets, the tweets with visual media and positive emotional tone received more engagement on Twitter through retweets and favorites compared to those with a negative or neutral tone. This study also breaks new ground by recognizing that higher level of racial diversity does not facilitate higher level of counterspeech against hate speech and hate crime. Additionally, this study highlights the varying degrees of participation in counterspeech across different ethnic groups within Asian American community and underscores the importance of tailored strategies in addressing hate speech. Recognizing this distinction proved essential in crafting evidence-based guidance for community and individual interventions while fostering support from allies of diverse racial backgrounds.
Louisa Ha, Ph.D. (Committee Chair)
Lisa Hanasono, Ph.D. (Committee Member)
Yanqin Lu, Ph.D. (Committee Member)
William Sawaya, Ph.D. (Other)
167 p.
Artificial Intelligence; Asian American Studies; Communication; Mass Media; South Asian Studies
Anti-Asian Hate; Counterspeech Strategies; Asian American; Hate speech; Hate crime; COVID-19; Machine Learning; Random Forest Model; Computational Analysis; Twitter Hashtag Analysis; Twitter Influencer;

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Citations

  • Kabir, M. E. (2023). #StopAsianHate Counterspeech on Twitter: Effectiveness of Counterspeech Strategies and Geospatial Analysis [Doctoral dissertation, Bowling Green State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169965912014087

    APA Style (7th edition)

  • Kabir, Md Enamul. #StopAsianHate Counterspeech on Twitter: Effectiveness of Counterspeech Strategies and Geospatial Analysis . 2023. Bowling Green State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169965912014087.

    MLA Style (8th edition)

  • Kabir, Md Enamul. "#StopAsianHate Counterspeech on Twitter: Effectiveness of Counterspeech Strategies and Geospatial Analysis ." Doctoral dissertation, Bowling Green State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu169965912014087

    Chicago Manual of Style (17th edition)

bgsu169965912014087
253
© 2023, all rights reserved.
This open access ETD is published by Bowling Green State University and OhioLINK.