论文标题
#China有多少仇恨？两年后，关于中国相关的仇恨推文的初步分析开始了
How Much Hate with #china? A Preliminary Analysis on China-related Hateful Tweets Two Years After the Covid Pandemic Began
论文作者
论文摘要
全球大流行爆发后，在线内容充满了仇恨言论。唐纳德·特朗普（Donald Trump）的“中国病毒”（Donald Trump）的推文将责任转移到了19日病毒向中国和中国人民传播的责备，这引发了新一轮的反中国仇恨在线和离线。这项研究旨在在大流行（2020年和2021年）爆发后的两年内在Twitter上检查中国相关的仇恨言论。通过Twitter的API，收集了总共2,172,333条推文#China在此期间发布的推文。通过使用多种最先进的审计语言模型进行仇恨言论检测，我们确定了各种类型的各种仇恨，从而自动标记了反中国仇恨言论数据集。我们确定2020年#China推文2.5％的仇恨率和2021年的1.9％。这远高于Gao等人在Twitter上在Twitter上在线仇恨言论的平均速度。我们对#China Tweets中仇恨言论的关键词分析揭示了可恨的#China Tweet中最常提到的术语，可用于进一步的社会科学研究。
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.
