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Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
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Nicholas Micallef ,
Yan Wu
Social Networks Analysis and Mining: 16th International Conference, ASONAM 2024, Volume: 15213 Lecture Notes in Computer Science, Pages: 211 - 229
Swansea University Authors:
Nicholas Micallef , Yan Wu
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DOI (Published version): 10.1007/978-3-031-78548-1_17
Abstract
This paper takes a multidisciplinary approach and conducts a three-dimensional analysis of #StopAsianHate tweets from January 1, 2021, to December 31, 2022 by combining computer science, applied linguistics, and cultural studies. It employs a ‘funnel approach’, from a broad examination to specific s...
| Published in: | Social Networks Analysis and Mining: 16th International Conference, ASONAM 2024 |
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| ISBN: | 9783031785474 9783031785481 |
| ISSN: | 0302-9743 1611-3349 |
| Published: |
Cham
Springer
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68757 |
| Abstract: |
This paper takes a multidisciplinary approach and conducts a three-dimensional analysis of #StopAsianHate tweets from January 1, 2021, to December 31, 2022 by combining computer science, applied linguistics, and cultural studies. It employs a ‘funnel approach’, from a broad examination to specific sentimental and linguistic dimensions within the top 10% most engaged tweets. The analysis reveals that the #StopAsianHate hashtag is primarily used for counter-discourse against Anti-Asian hate crime, expressing collective in-group identity and inclusionary out-group solidarity against racism. A key finding is the representation of Asian people as the ‘model minority’, derived from combined analyses of sentiments, politeness, toxicity, and Corpus-Assisted Critical Discourse Analysis of the tweets. The #StopAsianHate movement is characterised as moderate, evidenced by the large number of tweets with positive sentiment scores and frequent relational identification, which refers to anti-racism supporters as ‘friends’, ‘folks’, and ‘family’. Though negative sentiment scores are also prevalent, they are found non-toxic and can be explained by tweet genre’s rare use of polite expressions, as well as the prominence of #StopAsianHate thematic words such as ‘hate’, ‘racism’, and ‘crime’, serving as tools to challenge racism. Most notably, the study provides fresh insights into the growing self-reflective collective awareness of the negative impacts of ‘model minority’ stereotypes within the Asian communities and discusses ongoing opportunities and challenges in #StopAsianHate movement. |
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| Item Description: |
Lecture Notes in Computer Science, volume 15213 |
| Keywords: |
#StopAsianHate; model minority; social media; digital activism; sentiment analysis; corpus-assisted critical discourse analysis |
| College: |
Faculty of Humanities and Social Sciences |
| Funders: |
Morgan Advanced Studies Institute - MASI MID1001-104. |
| Start Page: |
211 |
| End Page: |
229 |