Publication:

Xin Tong*, Yixuan Li#, Jiayi Li#, Rongqi Bei#, and Luyao Zhang*. 2022. What are People Talking about in #BlackLivesMatter and #StopAsianHate? Exploring and Categorizing Twitter Topics Emerged in Online Social Movements through the Latent Dirichlet Allocation Model. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (AIES '22). Association for Computing Machinery: 723–738. https://doi.org/10.1145/3514094.3534202

Supplementary Materials:

arXiv:

https://arxiv.org/abs/2205.14725

GitHub:

https://github.com/hci-blockchain/blacklivesmatter

Abstract:

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.

Undergraduate Student Co-Authors at Duke Kunshan University

Yixuan Li

Yixuan is a creative technologist and digital designer based in Brooklyn, New York. She is currently a first-year master student at New York University, Interactive Telecommunications Program (ITP). She graduated from Duke Kunshan University with a bachelor degree in Data Science. She specializes in using technology to design solutions that are both functional and beautiful. Her expertise mainly lie in UI/UX design, creative coding, AI-driven design, virtual reality, and social computing.

Jiayi Li

Jiayi Li is a master’s student mathematics of finance at the Columbia University. She obtained her bachelor of science degree majoring in Data Science from Duke Kunshan University. She is particularly interested in quantitative research in the field of finance such as options trading.

Rongqi Bei

Rongqi Bei is a master’s student in the University of Michigan School of Information with a specialization in Human-Computer Interaction (HCI) research. She obtained her bachelor of science degree majoring in Data Science from Duke Kunshan University. She is particularly interested in understanding and applying technologies to transform people’s health-related behaviors and contribute to their well-being. She is good at exploring problems and developing design solutions with the human-centered approach, rendering her work meaningful to both the general public and marginalized groups. Rongqi is passionate and persistent, striving to go beyond and dive deep into the HCI academic field.