  Following the outbreak of a global pandemic, online content is filled with
hate speech. Donald Trump's ''Chinese Virus'' tweet shifted the blame for the
spread of the Covid-19 virus to China and the Chinese people, which triggered a
new round of anti-China hate both online and offline. This research intends to
examine China-related hate speech on Twitter during the two years following the
burst of the pandemic (2020 and 2021). Through Twitter's API, in total
2,172,333 tweets hashtagged #china posted during the time were collected. By
employing multiple state-of-the-art pretrained language models for hate speech
detection, we identify a wide range of hate of various types, resulting in an
automatically labeled anti-China hate speech dataset. We identify a hateful
rate in #china tweets of 2.5% in 2020 and 1.9% in 2021. This is well above the
average 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 and
those identified as hateful in 2020 and 2021 through visualizing the daily
number 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 #china
tweets, which can be used for further social science studies.
