We study the rise of Islamophobia on Twitter during the pandemic. We make public the CoronaIslam dataset precog.iiitd.edu.in/resources.html with 410,990 tweets from Feb-Mayโ20. 2000 tweets annotated as hateful and non-hateful for a binary Islamophobia-classifier
We find a link in online & offline behaviour with spikes in online tweets occurring around major real-world events. Most hateful tweets were made when news reports linked Tablighi Jamaat congregation to the spread of #COVID19. Graph with temporal spikes ๐ and table of events ๐
We study the change in context of words โMuslimโ and โVirusโ through the 4 months. We find terms like โVirusโ and โCovid19โ are used in a semantically similar context as โMuslimโ from March onwards! The term โVirusโ is also increasingly associated with โMuslimโ.
Topic modelling yields both hateful & non-hateful topics. Hateful topics revolve around Tablighi Jamaat & blame the Muslim community for spread of virus and attack of health workers. Non hateful topics included the topic of Ramadan when Muslims donated plasma & helped others!๐ค
We study user characteristics of users posting hateful & non-hateful tweets. We find an association b/w religion & how one perceives Muslims. Word clouds of bios of users w/ least to most no. of hateful tweets (L-R). Network graph shows separate communities based on hate-posting.
We study popular URLs referenced in CoronaIslam dataset.
We study content from @OpIndia_com@BBC@YouTube. OpIndia articles cover Tablighi Jamaat, BBC articles cover Uighur Muslims & COVID precautions.
From 0700 - 0830, we have 20,019 tweets (original, retweets, quotes). Around 9,075 unique users posted these tweets with 2,200 users posting original tweets. Most popular hashtags: #LokSabhaElections2019 14774, #TNElection2019 6153, #Elections2019, 3165, #Ajith 2342, #Thala 2186