Fig. 1: The Epidemic Psychology on Twitter during the first contagion wave.

A–C evolution of the use of different language categories over time in tweets related to COVID-19. Each row in the heatmaps represents a language category (e.g., words expressing anxiety) that our manual coding associated with one of the three psycho-social epidemics. The cell color represents the daily standardized fraction of people who used words related to that category: values that are higher than the average are red and those that are lower are blue. Categories are partitioned in three groups according to the type of psycho-social epidemics they model: Fear, Morality, and Action. D Average gradient (i.e., instantaneous variation) of all the language categories; the peaks of gradient identify change-points—dates around which a considerable change in the use of multiple language categories happened at once. The dashed vertical lines that cross all the plots represent these change-points. E–H temporal evolution of four families of indicators we used to corroborate the validity of the trends identified by the language categories. We checked internal validity by comparing the language categories with a custom keyword-search approach and two deep-learning NLP tools that extract types of social interactions and mentions of medical symptoms. We checked external validity by looking at mobility patterns in different venue categories as estimated by the GPS geo-localization service of the Foursquare mobile app. The timeline at the bottom of the figure marks some of the key events of the COVID-19 pandemic in the U.S. such as the announcements of the first infection of COVID-19 recorded.