#Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19

TL;DR

This study analyzes how negative sentiment and racist hashtags on Twitter evolved through the three stages of COVID-19, providing insights for targeted social media interventions to combat racism and xenophobia.

Contribution

It introduces a stage-based sentiment analysis framework to track racist sentiment evolution during COVID-19, informing stage-specific anti-racism policies.

Findings

Negative sentiment increased as COVID-19 progressed to a global pandemic.

Distinct themes of racism emerged at different stages of the outbreak.

Stage-specific intervention strategies are recommended based on sentiment trends.

Abstract

Situated in the global outbreak of COVID-19, our study enriches the discussion concerning the emergent racism and xenophobia on social media. With big data extracted from Twitter, we focus on the analysis of negative sentiment reflected in tweets marked with racist hashtags, as racism and xenophobia are more likely to be delivered via the negative sentiment. Especially, we propose a stage-based approach to capture how the negative sentiment changes along with the three development stages of COVID-19, under which it transformed from a domestic epidemic into an international public health emergency and later, into the global pandemic. At each stage, sentiment analysis enables us to recognize the negative sentiment from tweets with racist hashtags, and keyword extraction allows for the discovery of themes in the expression of negative sentiment by these tweets. Under this public health crisis of human beings, this stage-based approach enables us to provide policy suggestions for the enactment of stage-specific intervention strategies to combat racism and xenophobia on social media in a more effective way.