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