Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India During COVID-19 Infodemic

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.

A structured literature review of the health infodemic on social media in Africa

“…Social media provides a convenient channel for the spread of information during pandemics as it is easily accessible and can reach a wide audience (Jolly et al 2020 ). Using social media as a risk communication tool means that the barriers to public involvement in emergency responses are lowered.…”

“…During a pandemic, people use location-based social network services, such as Twitter or Facebook, that collect time-stamped and geo-located data for authorities that provide information about their environment in real time. The popularity of social media platforms has meant a sharp increase in their usage during the COVID-19 pandemic, as shown by the 45% increase in Twitter usage during the first 3 months of 2020 (Jolly et al 2020 ).…”

QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Media

“…Medford et al (2020) analyzed COVID-19 related tweets to understand different content types such as emotional, racially prejudiced, xenophobic or content that causes fear. Other recent work includes identifying low-credibility information using data from social media (Yang et al, 2020), detecting prejudice (Vidgen et al, 2020), finding challenges related to data, tools, and ethical issues (Ding et al, 2020), analyzing the spread of COVID-19 misinformation in relation to culture, society, and politics (Leng et al, 2021), detecting the spread of misleading information and the credibility of users who propagate it (Mourad et al, 2020), identifying positive influencers to propagate information (Pastor-Escuredo and Tarazona, 2020), analyzing the users who spread misinformation and the propagation of misinformation (Shahi et al, 2021), analyzing psychometric aspects in relation to the COVID-19 infodemic (Aggrawal et al, 2021), developing a multilingual COVID- The above research has focused on addressing one or more aspects of the infodemic (e.g., factuality). The work by Song et al (2021) addresses several aspects of COVID-19 related disinformation, where they collected false and misleading claims about COVID-19 from IFCN Poynter and annotated them as, public authority, community spread and impact, medical advice, self-treatments, and virus effects, prominent actors, conspiracies, virus transmission, virus origins and properties, public reaction, and vaccines, medical treatments, and tests.…”

Mining Trends of COVID-19 Vaccine Beliefs on Twitter With Lexical Embeddings: Longitudinal Observational Study

“…The COVID-19 pandemic has been studied in multidisciplinary aspects, and the analysis of Twitter posts remains a widely explored area in public health research [ 13 - 15 ], primarily because of the rapidly evolving nature of the content. Over the last decade, researchers have used multiple methods such as sentiment classification [ 16 ], social network analysis [ 17 ], and topic identification [ 18 ] to study the presence of provaccine and antivaccine communities on social media.…”