Influencers Against Hate: A Comparison of Counter Speech Strategies Among South Asian, East Asian, and Non Asian American Social Media Influencers.
This study challenges assumptions about the uniformity within the Asian American community, especially in countering anti-Asian hate. Diving into the cultural intricacies among East Asian, South Asian, and non-Asian American influencers on X (formerly Twitter) and based on social identity theory, th...
| Publicado en: | Howard Journal of Communications Vol. 37; no. 2; pp. 543 - 562 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Taylor & Francis Ltd
Apr/May2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=193226952&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 193226952 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10646175 B7K jtl: Howard Journal of Communications issn: 10646175 maglogo: N pubinfo: dt: Apr/May2026 vid: 37 iid: 2 pid: 377 pub: Taylor & Francis Ltd artinfo: ui: 193226952 10.1080/10646175.2025.2494271 ppf: 543 ppct: 19 formats: tig: atl: Influencers Against Hate: A Comparison of Counter Speech Strategies Among South Asian, East Asian, and Non Asian American Social Media Influencers. aug: au: Kabir, Md Enamul Ha, Louisa affil: Department of Communication, Clemson University, Clemson, South Carolina, USA School of Media and Communication, Bowling Green State University, Bowling Green, Ohio, USA su: United States Twitter (Web resource) Speech Crime Content analysis Emotions Mass media Sentiment analysis Psychosocial factors Celebrities COVID-19 pandemic Speech evaluation Random forest algorithms Pearson correlation (Statistics) East Asians Statistical sampling Research evaluation Descriptive statistics Chi-squared test South Asians Support vector machines Statistics Research methodology Accuracy Inter-observer reliability sug: subj: Speech Crime Content analysis Emotions Mass media Sentiment analysis Psychosocial factors Celebrities COVID-19 pandemic United States Marketing Research and Public Opinion Polling Speech evaluation Random forest algorithms Pearson correlation (Statistics) East Asians Statistical sampling Research evaluation Descriptive statistics Chi-squared test South Asians Support vector machines Statistics Research methodology Accuracy Inter-observer reliability Twitter (Web resource) keyword: Anti-Asian hate Asian American identity Computational communication Counter speech strategy Social media influencers Anti-Asian hate Asian American identity Computational communication Counter speech strategy Social media influencers ab: This study challenges assumptions about the uniformity within the Asian American community, especially in countering anti-Asian hate. Diving into the cultural intricacies among East Asian, South Asian, and non-Asian American influencers on X (formerly Twitter) and based on social identity theory, this study revealed compelling differences in counter speech strategies between South Asian and East Asian influencers. Combining computational techniques such as random forest machine learning model, and manual content analysis, 106,388 tweets associated with the hashtag #StopAsianHate were analyzed. The much lower incidence of counter speech among South Asian influencers warrants attention about their sense of exclusion from the broader Asian identity. The noticeable contrast in visual media use indicates nuanced differences influenced by subgroup affiliations. This research emphasizes the need to understand the disconnect between South Asians and East Asians and to explore the factors influencing their choice of counter speech strategies. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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