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Transcending the binary categorization of racist and xenophobic texts, thisresearch takes cues from social science theories to develop a four dimensionalcategory for racism and xenophobia detection, namely stigmatization,offensiveness, blame, and exclusion. With the aid of deep learning techniques,this categorical detection enables insights into the nuances of emergent topicsreflected in racist and xenophobic expression on Twitter. Moreover, a stagewise analysis is applied to capture the dynamic changes of the topics acrossthe stages of early development of Covid-19 from a domestic epidemic to aninternational public health emergency, and later to a global pandemic. The maincontributions of this research include, first the methodological advancement.By bridging the state-of-the-art computational methods with social scienceperspective, this research provides a meaningful approach for future researchto gain insight into the underlying subtlety of racist and xenophobicdiscussion on digital platforms. Second, by enabling a more accuratecomprehension and even prediction of public opinions and actions, this researchpaves the way for the enactment of effective intervention policies to combatracist crimes and social exclusion under Covid-19.

Beyond a binary of (non)racist tweets: A four-dimensional categorical  detection and analysis of racist and xenophobic opinions on Twitter in early  Covid-19