论文标题
COVID-19期间的多维种族主义分类：污名化，进攻性，责备和排斥
Multi-dimensional Racism Classification during COVID-19: Stigmatization, Offensiveness, Blame, and Exclusion
论文作者
论文摘要
超越种族主义文本的二元分类，我们的研究从社会科学理论中获取线索，以开发一种多维模型，用于种族主义检测，即污名化，进攻性，责备和排斥。借助BERT和主题建模，该分类检测可以洞悉Covid-19期间数字平台上种族主义讨论的基本巧妙之处。我们的研究有助于丰富有关社交媒体上种族主义行为的学术讨论。首先，采用阶段分析来捕获在Covid-19的早期阶段的主题变化的动态，该阶段从国内流行病转变为国际公共卫生紧急情况，后来转变为全球大流行。此外，映射这一趋势可以更准确地预测有关离线世界中种族主义的公众舆论发展，同时，制定了规定的干预策略，以打击像Covid-19这样的全球公共卫生危机期间的种族主义兴起。此外，这项跨学科研究还指出了关于社交网络分析和采矿的未来研究的方向。将社会科学观点整合到计算方法的发展中，为更准确的数据检测和分析提供了见解。
Transcending the binary categorization of racist texts, our study takes cues from social science theories to develop a multi-dimensional model for racism detection, namely stigmatization, offensiveness, blame, and exclusion. With the aid of BERT and topic modeling, this categorical detection enables insights into the underlying subtlety of racist discussion on digital platforms during COVID-19. Our study contributes to enriching the scholarly discussion on deviant racist behaviours on social media. First, a stage-wise analysis is applied to capture the dynamics of the topic changes across the early stages of COVID-19 which transformed from a domestic epidemic to an international public health emergency and later to a global pandemic. Furthermore, mapping this trend enables a more accurate prediction of public opinion evolvement concerning racism in the offline world, and meanwhile, the enactment of specified intervention strategies to combat the upsurge of racism during the global public health crisis like COVID-19. In addition, this interdisciplinary research also points out a direction for future studies on social network analysis and mining. Integration of social science perspectives into the development of computational methods provides insights into more accurate data detection and analytics.
