“A Virus Has No Religion”: Analyzing Islamophobia on Twitter During the COVID-19 Outbreak
Complete page-faithful edition converted from the ACM version of record.- doi
- 10.1145/3465336.3475111
- url
- https://doi.org/10.1145/3465336.3475111
- name
- “A Virus Has No Religion”: Analyzing Islamophobia on Twitter During the COVID-19 Outbreak
- pages
- 11
- format
- page-faithful facsimile
- acm_url
- https://dl.acm.org/doi/10.1145/3465336.3475111
- authors
- 1.Mohit Chandra2.Manvith Reddy3.Shradha Sehgal4.Saurabh Gupta5.Arun Balaji Buduru6.Ponnurangam Kumaraguru
- doi_url
- https://doi.org/10.1145/3465336.3475111
- license
- restricted
- summary
- Complete page-faithful edition converted from the ACM version of record.
- 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 commu- nities 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 longitudi- nal 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 patri- otism 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.
- keywords
- social network analysis; data mining; web mining; social media
- source_pdf
- HT-2021_35-30_3465336/3465336.3475111.pdf
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- full_text
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- publication
- Proceedings of the 32nd ACM Conference on Hypertext and Social Media (HT ’21)
- ccs_concepts
- • Human-centered computing →Empirical studies in collab-
- displayAuthor
- Mohit Chandra, Manvith Reddy, Shradha Sehgal, Saurabh Gupta, Arun Balaji Buduru, Ponnurangam Kumaraguru
- source_sha256
- f6c81f836254cf5b16c70d443f477896c2f314a7a306d6b2a9c6ab581c7f31f2
- publication_year
- 2021
- source_attribution
- Complete page images converted from the ACM version of record under supplied ACM publication authorization.
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