# Multilingualism, Transnationality, and K-pop in the Online #StopAsianHate Movement

arXiv: https://arxiv.org/abs/2503.02707 · HTML: https://mlsanity.com/paper/2503.02707/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 63 references that are in the map.
- First posted on arXiv 4 Mar 2025.

## Builds on (most cited first, 10 of 10)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Tired of Topic Models? Clusters of Pretrained Word Embeddings Make for Fast and Good Topics too!](https://mlsanity.com/paper/2004.14914/) (2020)
- [Analyzing Polarization in Social Media: Method and Application to Tweets on 21 Mass Shootings](https://mlsanity.com/paper/1904.01596/) (2019)
- [Topic Modeling with Contextualized Word Representation Clusters](https://mlsanity.com/paper/2010.12626/) (2020)
- [Is Neural Topic Modelling Better than Clustering? An Empirical Study on Clustering with Contextual Embeddings for Topics](https://mlsanity.com/paper/2204.09874/) (2022)
- [Media Cloud: Massive Open Source Collection of Global News on the Open Web](https://mlsanity.com/paper/2104.03702/) (2021)
- [A Large-Scale Empirical Study of Geotagging Behavior on Twitter](https://mlsanity.com/paper/1908.10948/) (2019)
- [Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021](https://mlsanity.com/paper/2009.00596/) (2020)
- [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](https://mlsanity.com/paper/2205.14725/) (2022)
- [Parasocial diffusion: K-pop fandoms help drive COVID-19 public health messaging on social media](https://mlsanity.com/paper/2110.04149/) (2021)

## Abstract (arXiv)

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.