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Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter

Yuze Sha Orcid Logo, Nicholas Micallef Orcid Logo, Yan Wu Orcid Logo

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 Orcid Logo, Yan Wu Orcid Logo

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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...

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Published in: Social Networks Analysis and Mining: 16th International Conference, ASONAM 2024
ISBN: 9783031785474 9783031785481
ISSN: 0302-9743 1611-3349
Published: Cham Springer 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68757
first_indexed 2025-01-30T16:02:05Z
last_indexed 2025-12-10T05:26:21Z
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It employs a &#x2018;funnel approach&#x2019;, 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 &#x2018;model minority&#x2019;, 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 &#x2018;friends&#x2019;, &#x2018;folks&#x2019;, and &#x2018;family&#x2019;. Though negative sentiment scores are also prevalent, they are found non-toxic and can be explained by tweet genre&#x2019;s rare use of polite expressions, as well as the prominence of #StopAsianHate thematic words such as &#x2018;hate&#x2019;, &#x2018;racism&#x2019;, and &#x2018;crime&#x2019;, serving as tools to challenge racism. 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spelling 2025-12-09T21:41:33.1599001 v2 68757 2025-01-29 Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter 1cc4c84582d665b7ee08fb16f5454671 0000-0002-2683-8042 Nicholas Micallef Nicholas Micallef true false fcb0b08dd7afa00f6899a02d4cb66fff 0000-0002-5741-6862 Yan Wu Yan Wu true false 2025-01-29 MACS 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. Conference Paper/Proceeding/Abstract Social Networks Analysis and Mining: 16th International Conference, ASONAM 2024 15213 Lecture Notes in Computer Science 211 229 Springer Cham 9783031785474 9783031785481 0302-9743 1611-3349 #StopAsianHate; model minority; social media; digital activism; sentiment analysis; corpus-assisted critical discourse analysis 24 1 2025 2025-01-24 10.1007/978-3-031-78548-1_17 Lecture Notes in Computer Science, volume 15213 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required Morgan Advanced Studies Institute - MASI MID1001-104. 2025-12-09T21:41:33.1599001 2025-01-29T20:50:55.1130290 Faculty of Humanities and Social Sciences School of Culture and Communication - Media, Communications, Journalism and PR Yuze Sha 0000-0001-9788-8250 1 Nicholas Micallef 0000-0002-2683-8042 2 Yan Wu 0000-0002-5741-6862 3 68757__33547__a066879cbb5445439eeb37077ea75e7f.pdf 68757.AAM.pdf 2025-02-10T09:57:42.3477201 Output 563967 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
spellingShingle Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
Nicholas Micallef
Yan Wu
title_short Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
title_full Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
title_fullStr Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
title_full_unstemmed Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
title_sort Dissecting the Advocacy Discourse Behind the #StopAsianHate Movement on X/Twitter
author_id_str_mv 1cc4c84582d665b7ee08fb16f5454671
fcb0b08dd7afa00f6899a02d4cb66fff
author_id_fullname_str_mv 1cc4c84582d665b7ee08fb16f5454671_***_Nicholas Micallef
fcb0b08dd7afa00f6899a02d4cb66fff_***_Yan Wu
author Nicholas Micallef
Yan Wu
author2 Yuze Sha
Nicholas Micallef
Yan Wu
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description 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.
published_date 2025-01-24T05:26:21Z
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