Tracking collective emotions in sixteen countries during COVID-19: A novel methodology for identifying major emotional events using Twitter

Using messages posted on Twitter, this research developed a new methodology for estimating collective emotions (CEs) within countries. It applied time-series analytic methodology to develop and demonstrate a novel application of CEs to identify emotional events that are significant at the societal level. The study analyzed over 200 million words from over 10 million Twitter messages posted in sixteen countries during the first 120 days of the COVID-19 pandemic. Daily levels of collective anxiety and positive emotions were estimated using Linguistic Inquiry and Word Count’s (LIWC) psychologically validated lexicon. The resulting time series estimates of both collective emotions were analyzed for structural breaks which mark abrupt changes in a series due to external shocks. External shocks to collective emotions come from events that are of shared emotional relevance and the analysis of structural breaks showed that a reduction in collective anxiety and increase in collective positive emotions in most countries followed WHO’s declaration of the COVID-19 situation as a global pandemic. Announcements of economic support packages and social restrictions also had similar impacts in countries. This indicated that reduction of uncertainties around the rapidly evolving COVID-19 situation during the first 120 days of the pandemic had a positive emotional impact on people in all the countries in the study. The study contributes to the field of CEs and applied research on collective psychological phenomena