๐ŸŽ‰๐ŸŽ‰ Our paper โ€œA Virus Has No Religionโ€: Analyzing Islamophobia on Twitter During the COVID-19 Outbreak, accepted at @ACMHT ๐ŸŽ‰๐ŸŽ‰ Work with amazing students @mohit__30 @ManvithReddy18 @shradhasgl @reallysaurabh #IIITStudentsRock
Pre-print: arxiv.org/pdf/2107.05104โ€ฆ
Findings ๐Ÿงต
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-Mayโ€™20. 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 ๐Ÿ“‰ and table of events ๐Ÿ‘‡
We study the change in context of words โ€˜Muslimโ€™ and โ€˜Virusโ€™ through the 4 months. We find terms like โ€˜Virusโ€™ and โ€˜Covid19โ€™ are used in a semantically similar context as โ€˜Muslimโ€™ from March onwards! The term โ€˜Virusโ€™ is also increasingly associated with โ€˜Muslimโ€™.
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!๐Ÿค
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.
Overall, we find a rise in hate sentiments towards Muslims during the pandemic. Shoutout to other works exploring racism associated with COVID.
Sinophobia studied by @zsavvas90 @jhblackb @gianluca_string @ciciling07 @realyangzhang @CISPA @iDRAMALab ๐Ÿ™๐Ÿ™
Asian hate studied by @cjziems, @soni_sandeep @srijaankedia @GeorgiaTech ๐Ÿ™๐Ÿ™
Our dataset is also obtained by filtering a larger COVID-19 dataset by @thatmchen @KristinaLerman @emilio__ferrara
Tagging organizations that fight hate-speech: @adl @APC_News @hatespeechbeda @officialuarh @nohate_speech @AkyolinEnglish

Paper again ๐Ÿ“œ : arxiv.org/pdf/2107.05104โ€ฆ

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More from @ponguru

18 Apr 19
First tweet (not posted by a political handle or advertisments) in our data collection with #loksabhaelections2019! First tweet with inked status! #Elections2019 #SocialMedia #VotingRound2 #IndiaElections2019 #ProfGiri #Research #TNElection2019 #OruviralPuratchi
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
Most active users with their number of tweets (original, retweets, quotes) posted,
('@RanjanaSiroha', 76),
('@ThalaDhas', 70),
('@Ebinson18', 64),
('@PsYcho_bwy', 61),
('@Thala15111', 59)
Read 21 tweets

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