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.

本研究通过使用Twitter API和多个预训练语言模型，检测分析了自2020年以来标记为#china的推文中的仇恨言论，结果显示在2020年和2021年这两年中，这些言论的比例分别为2.5%和1.9%，高于Twitter上平均的0.6%。同时，研究者通过视觉展示也分析了这些仇恨言论的日增长率和趋势。该研究的关键词包括但不限于仇恨言论、Twitter、中国、新冠病毒和语言模型。

新冠疫情发生后两年与中国有关的仇恨推文初步分析