  Transcending the binary categorization of racist and xenophobic texts, this
research takes cues from social science theories to develop a four dimensional
category 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 topics
reflected in racist and xenophobic expression on Twitter. Moreover, a stage
wise analysis is applied to capture the dynamic changes of the topics across
the stages of early development of Covid-19 from a domestic epidemic to an
international public health emergency, and later to a global pandemic. The main
contributions of this research include, first the methodological advancement.
By bridging the state-of-the-art computational methods with social science
perspective, this research provides a meaningful approach for future research
to gain insight into the underlying subtlety of racist and xenophobic
discussion on digital platforms. Second, by enabling a more accurate
comprehension and even prediction of public opinions and actions, this research
paves the way for the enactment of effective intervention policies to combat
racist crimes and social exclusion under Covid-19.
