Multilingualism, Transnationality, and K-pop in the Online #StopAsianHate Movement
- URL: http://arxiv.org/abs/2503.02707v1
- Date: Tue, 04 Mar 2025 15:21:22 GMT
- Title: Multilingualism, Transnationality, and K-pop in the Online #StopAsianHate Movement
- Authors: Tessa Masis, Zhangqi Duan, Weiai Wayne Xu, Ethan Zuckerman, Jane Yeahin Pyo, Brendan O'Connor,
- Abstract summary: We present an analysis of 6.5 million "#StopAsianHate" tweets from 2.2 million users all over the globe and spanning 60 different languages.<n>We discover clear differences in events driving topics, where spikes in English tweets are driven by violent crimes in the US but spikes in non-English tweets are driven by transnational incidents of anti-Asian sentiment towards symbolic representatives of Asian nations.
- Score: 2.469628862140415
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The #StopAsianHate (SAH) movement is a broad social movement against violence targeting Asians and Asian Americans, beginning in 2021 in response to racial discrimination related to COVID-19 and sparking worldwide conversation about anti-Asian hate. However, research on the online SAH movement has focused on English-speaking participants so the spread of the movement outside of the United States is largely unknown. In addition, there have been no long-term studies of SAH so the extent to which it has been successfully sustained over time is not well understood. We present an analysis of 6.5 million "#StopAsianHate" tweets from 2.2 million users all over the globe and spanning 60 different languages, constituting the first study of the non-English and transnational component of the online SAH movement. Using a combination of topic modeling, user modeling, and hand annotation, we identify and characterize the dominant discussions and users participating in the movement and draw comparisons of English versus non-English topics and users. We discover clear differences in events driving topics, where spikes in English tweets are driven by violent crimes in the US but spikes in non-English tweets are driven by transnational incidents of anti-Asian sentiment towards symbolic representatives of Asian nations. We also find that global K-pop fans were quick to adopt the SAH movement and, in fact, sustained it for longer than any other user group. Our work contributes to understanding the transnationality and evolution of the SAH movement, and more generally to exploring upward scale shift and public attention in large-scale multilingual online activism.
Related papers
- SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness [73.73883111570458]
We introduce the first multilingual Event Extraction framework for extracting epidemic event information for a wide range of diseases and languages.
Annotating data in every language is infeasible; thus we develop zero-shot cross-lingual cross-disease models.
Our framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 from Chinese Weibo posts without any training in Chinese.
arXiv Detail & Related papers (2024-10-24T03:03:54Z) - A Community-Centric Perspective for Characterizing and Detecting Anti-Asian Violence-Provoking Speech [31.98433210638392]
Violence-provoking speech contributed to a massive surge in anti-Asian crimes during the pandemic.
We develop a codebook to characterize anti-Asian violence-provoking speech and collect a community-sourced dataset.
We show that accurate and reliable detection of violence-provoking speech is a challenging task.
arXiv Detail & Related papers (2024-07-21T17:27:17Z) - White Men Lead, Black Women Help? Benchmarking Language Agency Social Biases in LLMs [58.27353205269664]
Social biases can manifest in language agency.
We introduce the novel Language Agency Bias Evaluation benchmark.
We unveil language agency social biases in 3 recent Large Language Model (LLM)-generated content.
arXiv Detail & Related papers (2024-04-16T12:27:54Z) - Russo-Ukrainian War: Prediction and explanation of Twitter suspension [47.61306219245444]
This study focuses on the Twitter suspension mechanism and the analysis of shared content and features of user accounts that may lead to this.
We have obtained a dataset containing 107.7M tweets, originating from 9.8 million users, using Twitter API.
Our results reveal scam campaigns taking advantage of trending topics regarding the Russia-Ukrainian conflict for Bitcoin fraud, spam, and advertisement campaigns.
arXiv Detail & Related papers (2023-06-06T08:41:02Z) - What are People Talking about in #BlackLivesMatter and #StopAsianHate?
Exploring and Categorizing Twitter Topics Emerging in Online Social Movements
through the Latent Dirichlet Allocation Model [27.53788299995914]
Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter.
This study adopts a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics.
We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics.
arXiv Detail & Related papers (2022-05-29T17:29:40Z) - "Stop Asian Hate!" : Refining Detection of Anti-Asian Hate Speech During
the COVID-19 Pandemic [2.5227595609842206]
COVID-19 pandemic has fueled a surge in anti-Asian xenophobia and prejudice.
We create and annotate a corpus of Twitter tweets using 2 experimental approaches to explore anti-Asian abusive and hate speech.
arXiv Detail & Related papers (2021-12-04T06:55:19Z) - Annotators with Attitudes: How Annotator Beliefs And Identities Bias
Toxic Language Detection [75.54119209776894]
We investigate the effect of annotator identities (who) and beliefs (why) on toxic language annotations.
We consider posts with three characteristics: anti-Black language, African American English dialect, and vulgarity.
Our results show strong associations between annotator identity and beliefs and their ratings of toxicity.
arXiv Detail & Related papers (2021-11-15T18:58:20Z) - Predicting Anti-Asian Hateful Users on Twitter during COVID-19 [7.788173128266611]
We apply natural language processing techniques to characterize social media users who began to post anti-Asian hate messages during COVID-19.
It is possible to predict who later publicly posted anti-Asian slurs.
arXiv Detail & Related papers (2021-09-15T13:49:37Z) - When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
We present CovidEmo, 1K tweets labeled with emotions.
We examine how well large pre-trained language models generalize across domains and crises.
arXiv Detail & Related papers (2021-07-23T04:07:14Z) - Analyzing COVID-19 on Online Social Media: Trends, Sentiments and
Emotions [44.92240076313168]
We analyze the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020.
By contrasting two very different countries, China and the Unites States, we reveal sharp differences in people's views on COVID-19 in different cultures.
Our study provides a computational approach to unveiling public emotions and concerns on the pandemic in real-time, which would potentially help policy-makers better understand people's need and thus make optimal policy.
arXiv Detail & Related papers (2020-05-29T09:24:38Z) - Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media
during the COVID-19 Crisis [51.39895377836919]
COVID-19 has sparked racism and hate on social media targeted towards Asian communities.
We study the evolution and spread of anti-Asian hate speech through the lens of Twitter.
We create COVID-HATE, the largest dataset of anti-Asian hate and counterspeech spanning 14 months.
arXiv Detail & Related papers (2020-05-25T21:58:09Z) - Detecting East Asian Prejudice on Social Media [10.647940201343575]
We report on the creation of a classifier that detects and categorizes social media posts from Twitter into four classes: Hostility against East Asia, Criticism of East Asia, Meta-discussions of East Asian prejudice and a neutral class.
arXiv Detail & Related papers (2020-05-08T08:53:47Z) - A Framework for the Computational Linguistic Analysis of Dehumanization [52.735780962665814]
We analyze discussions of LGBTQ people in the New York Times from 1986 to 2015.
We find increasingly humanizing descriptions of LGBTQ people over time.
The ability to analyze dehumanizing language at a large scale has implications for automatically detecting and understanding media bias as well as abusive language online.
arXiv Detail & Related papers (2020-03-06T03:02:12Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.