Online extremism and Islamophobic language and sentiment when discussing the COVID-19 pandemic and misinformation on Twitter

Abstract

This paper looks at the profiles of those who engaged in Islamophobic language/extremist behaviour on Twitter during the COVID-19 pandemic. This two-part analysis takes into account factors such as anonymity, membership length and postage frequency on language use, and the differences in sentiment expressed between pro-social and anti-social tweets. Analysis includes comparisons between low, moderate and high levels of anonymity, postage frequency and membership length, allowing for differences in keyword use to be explored. Our findings suggest that increased anonymity is not associated with an increase in Islamophobic language and misinformation. The sentiment analysis indicated that emotions such as anger, disgust, fear, sadness and trust were significantly more associated with pro-social Twitter users whereas sentiments such as anticipation, joy and surprise were significantly more associated with anti-social Twitter users. In some cases, evidence for joy in the suffering of others as a result of the pandemic was expressed.