  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.
