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

TL;DR

This paper introduces a four-dimensional categorization of racist and xenophobic tweets on Twitter during early Covid-19, combining social science insights with deep learning to analyze nuanced expressions and their evolution over time.

Contribution

It develops a novel four-dimensional detection framework for racism and xenophobia, integrating social science theories with deep learning for nuanced analysis.

Findings

Identified four key dimensions: stigmatization, offensiveness, blame, exclusion.

Tracked the evolution of racist and xenophobic expressions across Covid-19 stages.

Provided insights for targeted intervention policies.

Abstract

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.