# 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

arXiv: https://arxiv.org/abs/2205.14725 · HTML: https://mlsanity.com/paper/2205.14725/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 80 references that are in the map.
- First posted on arXiv 29 May 2022.

## Builds on (most cited first, 8 of 8)

- [Divergent discourse between protests and counter-protests: #BlackLivesMatter and #AllLivesMatter](https://mlsanity.com/paper/1606.06820/) (2016)
- [Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement](https://mlsanity.com/paper/2010.06472/) (2020)
- [Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2021](https://mlsanity.com/paper/2009.00596/) (2020)
- [What Truly Matters? Using Linguistic Cues for Analyzing the #BlackLivesMatter Movement and its Counter Protests: 2013 to 2020](https://mlsanity.com/paper/2109.12192/) (2021)
- [COVID, BLM, and the polarization of US politicians on Twitter](https://mlsanity.com/paper/2008.03263/) (2020)
- [A Large-Scale Study of the Twitter Follower Network to Characterize the Spread of Prescription Drug Abuse Tweets](https://mlsanity.com/paper/2102.08661/) (2021)
- [State-level Racially Motivated Hate Crimes Contrast Public Opinion on the #StopAsianHate and #StopAAPIHate Movement](https://mlsanity.com/paper/2104.14536/) (2021)
- [Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions](https://mlsanity.com/paper/2002.02353/) (2020)

## Built on by (most cited first, 2 of 2)

- [AI Ethics on Blockchain: Topic Analysis on Twitter Data for Blockchain Security](https://mlsanity.com/paper/2212.06951/) (2022)
- [Multilingualism, Transnationality, and K-pop in the Online #StopAsianHate Movement](https://mlsanity.com/paper/2503.02707/) (2025)

## Abstract (arXiv)

Minority groups have been using social media to organize social movements that create profound social impacts. Black Lives Matter (BLM) and Stop Asian Hate (SAH) are two successful social movements that have spread on Twitter that promote protests and activities against racism and increase the public's awareness of other social challenges that minority groups face. However, previous studies have mostly conducted qualitative analyses of tweets or interviews with users, which may not comprehensively and validly represent all tweets. Very few studies have explored the Twitter topics within BLM and SAH dialogs in a rigorous, quantified and data-centered approach. Therefore, in this research, we adopted a mixed-methods approach to comprehensively analyze BLM and SAH Twitter topics. We implemented (1) the latent Dirichlet allocation model to understand the top high-level words and topics and (2) open-coding analysis to identify specific themes across the tweets. We collected more than one million tweets with the #blacklivesmatter and #stopasianhate hashtags and compared their topics. Our findings revealed that the tweets discussed a variety of influential topics in depth, and social justice, social movements, and emotional sentiments were common topics in both movements, though with unique subtopics for each movement. Our study contributes to the topic analysis of social movements on social media platforms in particular and the literature on the interplay of AI, ethics, and society in general.