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Abstract
COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people’s health and governance systems. Managing this infodemic not only requires mitigating misinformation but also an early understanding of underlying psychological patterns. In this study, we present a novel epidemic response management strategy. We analyze the psychometric impact and coupling of COVID-19 infodemic with official COVID-19 bulletins at the national and state level in India. We looked at them from the psycholinguistic lens of emotions and quantified the extent and coupling between them. We modified Empath, a deep skipgram-based lexicon builder, for effective capture of health-related emotions. Using this, we analyzed the lead-lag relationships between the time-evolution of these emotions in social media and official bulletins using Granger’s causality. It showed that state bulletins led the social media for some emotions such as Medical Emergency. In contrast, social media led the government bulletins for some topics such as hygiene, government, fun, and leisure. Further insights potentially relevant for policymakers and communicators engaged in mitigating misinformation are also discussed. We also introduce CoronaIndiaDataset, the first social-media-based Indian COVID-19 dataset at the national and state levels with over 5.6 million national and 2.6 million state-level tweets for the first wave of COVID-19 in India and 1.2 million national tweets for the second wave of COVID-19 in India.
Highlights
COVID-19 was declared a pandemic by the WHO on March 11, 2020
The analysis in this work focuses on the first wave of COVID-19, because of the limited availability of government bulletins in the second wave
The most common categories being discussed in the tweets were “medical emergency”, “government”, and “health”, which reflects that while discussing the pandemic, the public is bringing the government into the discourse, be it referring to some government policy or some information released by the government
Summary
COVID-19 was declared a pandemic by the WHO on March 11, 2020. Months before this announcement, the (mis)information surrounding COVID-19 was already raging across the world, and the “infodemic” threat was announced on February 15, 2020, at the Munich Security Conference. The excessive volume and velocity of (mis)information can put global systems at risk by compromising access to accurate, reliable, and trustworthy guidance when needed.21https://www.who.int/dg/speeches/detail/munich-security-conference. 2https://bit.ly/COVID19-infodemic.Analyzing Government and Twitter EmotionsSocial media has a major contribution to the spread of the infodemic. COVID-19 was declared a pandemic by the WHO on March 11, 2020. Months before this announcement, the (mis)information surrounding COVID-19 was already raging across the world, and the “infodemic” threat was announced on February 15, 2020, at the Munich Security Conference.. Twitter is the most popular microblogging platform for expressing public opinions. It has seen a sharp 45% increase in the usage of its curated events page since March 6, 2020, during the COVID-19 emergency. The massive volume of COVID-19 data provides an opportunity to use data mining techniques to understand the trends in language patterns indicative of government and public emotion
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