"A Virus Has No Religion": Analyzing Islamophobia on Twitter During the COVID-19 Outbreak

TL;DR

This study analyzes the surge of Islamophobia on Twitter during COVID-19, linking online hate speech with offline events, and introduces the CoronaBias dataset for large-scale quantitative analysis.

Contribution

First large-scale quantitative analysis of COVID-19 related Islamophobia on Twitter, including the creation of the CoronaBias dataset and multi-faceted analysis of content and user behavior.

Findings

Correlation between online Islamophobia and offline events.

Prevalence of anti-Muslim rhetoric in Indian sub-continent.

Content analysis shows increased toxicity and use of religious symbolism.

Abstract

The COVID-19 pandemic has disrupted people's lives driving them to act in fear, anxiety, and anger, leading to worldwide racist events in the physical world and online social networks. Though there are works focusing on Sinophobia during the COVID-19 pandemic, less attention has been given to the recent surge in Islamophobia. A large number of positive cases arising out of the religious Tablighi Jamaat gathering has driven people towards forming anti-Muslim communities around hashtags like #coronajihad, #tablighijamaatvirus on Twitter. In addition to the online spaces, the rise in Islamophobia has also resulted in increased hate crimes in the real world. Hence, an investigation is required to create interventions. To the best of our knowledge, we present the first large-scale quantitative study linking Islamophobia with COVID-19. In this paper, we present CoronaBias dataset which focuses on anti-Muslim hate spanning four months, with over 410,990 tweets from 244,229 unique users. We use this dataset to perform longitudinal analysis. We find the relation between the trend on Twitter with the offline events that happened over time, measure the qualitative changes in the context associated with the Muslim community, and perform macro and micro topic analysis to find prevalent topics. We also explore the nature of the content, focusing on the toxicity of the URLs shared within the tweets present in the CoronaBias dataset. Apart from the content-based analysis, we focus on user analysis, revealing that the portrayal of religion as a symbol of patriotism played a crucial role in deciding how the Muslim community was perceived during the pandemic. Through these experiments, we reveal the existence of anti-Muslim rhetoric around COVID-19 in the Indian sub-continent.