#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19

Abstract

The COVID-19 pandemic forced countries all over the world to take unprecedented measures, like nationwide lockdowns. To adequately understand the emotional and social repercussions, a large-scale reconstruction of how people perceived these unexpected events is necessary but currently missing. We address this gap through social media by introducing MERCURIAL (Multi-layer Co-occurrence Networks for Emotional Profiling), a framework which exploits linguistic networks of words and hashtags to reconstruct social discourse describing real-world events. We use MERCURIAL to analyse 101,767 tweets from Italy, the first country to react to the COVID-19 threat with a nationwide lockdown. The data were collected between the 11th and 17th March, immediately after the announcement of the Italian lockdown and the WHO declaring COVID-19 a pandemic. Our analysis provides unique insights into the psychological burden of this crisis, focussing on—(i) the Italian official campaign for self-quarantine (#iorestoacasa), (ii) national lockdown (#italylockdown), and (iii) social denounce (#sciacalli). Our exploration unveils the emergence of complex emotional profiles, where anger and fear (towards political debates and socio-economic repercussions) coexisted with trust, solidarity, and hope (related to the institutions and local communities). We discuss our findings in relation to mental well-being issues and coping mechanisms, like instigation to violence, grieving, and solidarity. We argue that our framework represents an innovative thermometer of emotional status, a powerful tool for policy makers to quickly gauge feelings in massive audiences and devise appropriate responses based on cognitive data.

Cognitive network science reconstructs how experts, news outlets and social media perceived the COVID-19 pandemic

“…The null model for emotional profiles was random word sampling from the NRC lexicon. This was necessary as the original NRC lexicon features more words eliciting certain emotions and fewer words eliciting other emotional states, so that richness of words by itself might be an artefact of the underlying dataset if no null model is considered (see also Stella et al 2020). Z-scores were computed between the empirical emotional profiles of "pandemic" and a distribution of 1000 random samples.…”

“…Social media strongly debated over the COVID-19 pandemic. Here attention was given to Italy, the first European country struck by COVID-19 and reacting with a national lockdown (see Stella et al 2020). In 37,500 Italian tweets (see Methods), "pandemic" spawned a social discourse featuring contrasting, affect-polarising concepts like "help", and "hope" together with "risk" and "attack".…”

“…Stella et al 2019). The identification of the emotional content of a given portion of language is called emotional profiling and it has been recently applied also to the investigation of online perceptions of COVID-19 (Stella et al 2020, Lwin et al 2020).…”

A Content Analysis of Coronavirus Tweets in the United States Just Prior to the Pandemic Declaration

“…For example, a lexicon-based emotional analysis highlighted how positive, neutral, and negative emotions coexisted within the social discourse in Italy after its nationwide lockdown. 10 This suggests a complex emotional response surrounding the outbreak. Similar complexities were found in Reddit feeds of individuals who tested positive for COVID-19.…”

Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks

“…For a recent network theoretical sentiment analysis of online writing see, for example,Stella et al (2020). For a review of network theoretical approaches to knowledge networks in education science seeSiew (2020).Frontiers in Education | www.frontiersin.org…”