Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter.
Stigmatization in social media: Documenting and analyzing hate speech for COVID-19 on Twitter.
复制标题
DOI：
10.1002/pra2.313
复制
发表时间：
2020-01-01
期刊：
影响因子：
--
通讯作者：
Yin, Zhanyuan
中科院分区：
文献类型：
--
作者：
Fan, Lizhou;Yu, Huizi;Yin, Zhanyuan
随着COVID-19大流行的展开，社交媒体上关于中国和中国人的仇恨言论助长了社会污名化。为了历史和人文的目的，这一正在形成的历史需要存档和分析。使用查询“中国+和+冠状病毒”从Twitter API中抓取，我们获得了3，457，402条关于中国的关键推文。在这个档案中，大约40%的推文来自美国，我们识别了25，467次仇恨言论，并使用机器学习和网络方法根据基于词汇的情感和人口统计数据对其进行分析。结果表明，仇恨言论的数量和情绪的示威活动，以及国家人口统计因素之间存在着实质性的关联。与贫困和失业率相关的惊讶和恐惧情绪十分突出。因此，这个数字档案和相关的分析不仅仅是历史性的。它们在提高公众认识和减轻未来危机方面发挥着至关重要的作用。因此，我们认为我们的研究是一个试点研究的分析方法，可能会被其他研究人员在各个领域使用。
As the COVID-19 pandemic has unfolded, Hate Speech on social media about China and Chinese people has encouraged social stigmatization. For the historical and humanistic purposes, this history-in-the-making needs to be archived and analyzed. Using the query "china+and+coronavirus" to scrape from the Twitter API, we have obtained 3,457,402 key tweets about China relating to COVID-19. In this archive, in which about 40% of the tweets are from the U.S., we identify 25,467 Hate Speech occurrences and analyze them according to lexicon-based emotions and demographics using machine learning and network methods. The results indicate that there are substantial associations between the amount of Hate Speech and demonstrations of sentiments, and state demographics factors. Sentiments of surprise and fear associated with poverty and unemployment rates are prominent. This digital archive and the related analyses are not simply historical, therefore. They play vital roles in raising public awareness and mitigating future crises. Consequently, we regard our research as a pilot study in methods of analysis that might be used by other researchers in various fields.