"Go eat a bat, Chang!": On the Emergence of Sinophobic Behavior on Web Communities in the Face of COVID-19
Fatemeh Tahmasbi, Leonard Schild, Chen Ling, Jeremy Blackburn, Gianluca Stringhini, Yang Zhang, Savvas Zannettou
Abstract
The outbreak of the COVID-19 pandemic has changed our lives in unprecedented ways. In the face of the projected catastrophic consequences, most countries have enacted social distancing measures in an attempt to limit the spread of the virus. Under these conditions, the Web has become an indispensable medium for information acquisition, communication, and entertainment. At the same time, unfortunately, the Web is being exploited for the dissemination of potentially harmful and disturbing content, such as the spread of conspiracy theories and hateful speech towards specific ethnic groups, in particular towards Chinese people and people of Asian descent since COVID-19 is believed to have originated from China. In this paper, we make a first attempt to study the emergence of Sinophobic behavior on the Web during the outbreak of the COVID-19 pandemic. We collect two large datasets from Twitter and 4chan’s Politically Incorrect board (/pol/) over a time period of approximately five months and analyze them to investigate whether there is a rise or important differences with regard to the dissemination of Sinophobic content. We find that COVID-19 indeed drives the rise of Sinophobia on the Web and that the dissemination of Sinophobic content is a cross-platform phenomenon: it exists on fringe Web communities like /pol/, and to a lesser extent on mainstream ones like Twitter. Using word embeddings over time, we characterize the evolution of Sinophobic slurs on both Twitter and /pol/. Finally, we find interesting differences in the context in which words related to Chinese people are used on the Web before and after the COVID-19 outbreak: on Twitter we observe a shift towards blaming China for the situation, while on /pol/ we find a shift towards using more (and new) Sinophobic slurs.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext 1f2acdc8-087b-456f-b1d2-ab57d8f0a92fCited by top-tier papers11
- You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic ContentXinlei He, Savvas Zannettou, Yun Shen, Yang ZhangS&P 2024 · 74 citations
- Why So Toxic?: Measuring and Triggering Toxic Behavior in Open-Domain ChatbotsWai Man Si, Michael Backes, Jeremy Blackburn, Emiliano De Cristofaro et al.CCS 2022 · 34 citations
- Understanding the Use of Images to Spread COVID-19 Misinformation on TwitterYuping Wang, Chen Ling, Gianluca StringhiniCSCW 2023 · 14 citations
- Discovering Differences in the Representation of People using Contextualized Semantic AxesLi Lucy, Divya Tadimeti, David BammanEMNLP 2022 · 7 citations
- Not All Asians are the Same: A Disaggregated Approach to Identifying Anti-Asian Racism in Social MediaFan Wu, Sanyam Lakhanpal, Qian Li, Kookjin Lee et al.WWW 2024 · 6 citations
Related papers
- Harm in Layers: Compositions of Misinformative Hate in Anti-Asian Speech and Their Impacts on Perceived HarmfulnessJiawei Zhou, Gaurav Verma, Lei Zhang, Nicholas Chang et al.CSCW 2025 · 2 citations
- Spatial-Temporal Analysis of Collective Emotional Resonance in China During Global Health CrisisLimiao Zhang, Xinyang Qi, Haiping Ma, Jie Gao et al.WWW 2025
- Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science OnlineCrystal Lee, Tanya Yang, Gabrielle D. Inchoco, Graham M. Jones et al.CHI 2021 · 140 citations
- "Positive Energy": Perceptions and Attitudes Towards COVID-19 Information on Social Media in ChinaZhicong Lu, Yue Jiang, Chenxinran Shen, Margaret C. Jack et al.CSCW 2021 · 16 citations
- "Here's Your Evidence": False Consensus in Public Twitter Discussions of COVID-19 ScienceAlexandros Efstratiou, Marina Efstratiou, Satrio Baskoro Yudhoatmojo, Jeremy Blackburn et al.CSCW 2024 · 3 citations
