On Xing Tian and the Perseverance of Anti-China Sentiment Online
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Abstract (arXiv)
Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time. The outbreak of COVID-19 and the extended quarantine has further amplified it. However, we lack a quantitative understanding of the cause of Sinophobia as well as how it evolves over time. In this paper, we conduct a large-scale longitudinal measurement of Sinophobia, between 2016 and 2021, on two mainstream and fringe Web communities. By analyzing 8B posts from Reddit and 206M posts from 4chan's /pol/, we investigate the origins, evolution, and content of Sinophobia. We find that, anti-Chinese content may be evoked by political events not directly related to China, e.g., the U.S. withdrawal from the Paris Agreement. And during the COVID-19 pandemic, daily usage of Sinophobic slurs has significantly increased even with the hate-speech ban policy. We also show that the semantic meaning of the words "China" and "Chinese" are shifting towards Sinophobic slurs with the rise of COVID-19 and remain the same in the pandemic period. We further use topic modeling to show the topics of Sinophobic discussion are pretty diverse and broad. We find that both Web communities share some common Sinophobic topics like ethnics, economics and commerce, weapons and military, foreign relations, etc. However, compared to 4chan's /pol/, more daily life-related topics including food, game, and stock are found in Reddit. Our finding also reveals that the topics related to COVID-19 and blaming the Chinese government are more prevalent in the pandemic period. To the best of our knowledge, this paper is the longest quantitative measurement of Sinophobia.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2022.
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Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 1.
10 papers.
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- 2024Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generationcited by 20
- 2024Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPTcited by 13
- 2025HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaignscited by 10
- 2023Comprehensive Assessment of Toxicity in ChatGPTcited by 2
- 2022How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Begancited by 1
- 2024WEIRD ICWSM: How Western, Educated, Industrialized, Rich, and Democratic is Social Computing Research?cited by 1
- 2025HateBench: Benchmarking Hate Speech Detectors on LLM-Generated Content and Hate Campaigns28 Jan 2025
- 2024WEIRD ICWSM: How Western, Educated, Industrialized, Rich, and Democratic is Social Computing Research?4 Jun 2024
- 2024Quite Good, but Not Enough: Nationality Bias in Large Language Models -- A Case Study of ChatGPT11 May 2024
- 2024Adversarial Nibbler: An Open Red-Teaming Method for Identifying Diverse Harms in Text-to-Image Generation14 Feb 2024
- 2023Comprehensive Assessment of Toxicity in ChatGPT3 Nov 2023
- 2023You Only Prompt Once: On the Capabilities of Prompt Learning on Large Language Models to Tackle Toxic Content10 Aug 2023
- 2023"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models7 Aug 2023
- 2023Unsafe Diffusion: On the Generation of Unsafe Images and Hateful Memes From Text-To-Image Models23 May 2023
- 2022On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning13 Dec 2022
- 2022How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began11 Nov 2022