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Following the outbreak of a global pandemic, online content is filled withhate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for thespread of the Covid-19 virus to China and the Chinese people, which triggered anew round of anti-China hate both online and offline. This research intends toexamine China-related hate speech on Twitter during the two years following theburst of the pandemic (2020 and 2021). Through Twitter's API, in total2,172,333 tweets hashtagged #china posted during the time were collected. Byemploying multiple state-of-the-art pretrained language models for hate speechdetection, we identify a wide range of hate of various types, resulting in anautomatically labeled anti-China hate speech dataset. We identify a hatefulrate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above theaverage rate of online hate speech on Twitter at 0.6% identified in Gao et al.,2017. We further analyzed the longitudinal development of #china tweets andthose identified as hateful in 2020 and 2021 through visualizing the dailynumber and hate rate over the two years. Our keyword analysis of hate speech in#china tweets reveals the most frequently mentioned terms in the hateful #chinatweets, which can be used for further social science studies.

How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began