Information Disorder Amidst Crisis: A Case Study of COVID-19 in India

Abstract

The devastation led by the COVID-19 pandemic was accompanied by a plethora of misinformation, laden with pseudoscience, hoaxes, and myths, often intertwined with hate speech. This phenomenon was particularly pronounced in India, where the intricate political and communal landscape provided fertile ground. The misinformation, with its elements of hate speech, posed a significant threat to societal cohesion. In response, this article delves into the dynamics of misinformation during the COVID-19 crisis in India, with a specific focus on differentiating general misinformation (GM) from hateful misinformation (HM). To this end, we construct an Indian COVID-19 misinformation dataset collected from various online social and mainstream media and analyze it from various perspectives. Mainly, we focus on temporal evolution, content and topics involved, and emotions and sentiment sensationalism of COVID-19 misinformation. We found the emotions of sadness and fear as key amplifiers of misinformation in general, with negative sentiments dominating HM. Through our comprehensive analysis, we found many such interesting insights and patterns. We also perform hate detection within misinformation content using various unsupervised and supervised learning techniques. Our results show that while GM is relatively easier to identify, it is challenging to detect HM. Overall, deep learning models are found to be more effective than unsupervised methods. By discovering key insights and patterns, this study serves as a foundation for developing robust strategies to combat information disorder.