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Open AccessArticle

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

Complex Science Consulting, 73100 Lecce, Italy
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
Human Complex Data Hub, School of Psychological Sciences, University of Melbourne, Melbourne, VIC 3010, Australia
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2020, 4(2), 14;
Received: 8 May 2020 / Revised: 5 June 2020 / Accepted: 6 June 2020 / Published: 16 June 2020
(This article belongs to the Special Issue Knowledge Modelling and Learning through Cognitive Networks)
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.
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Keywords: COVID-19; social media; hashtag networks; emotional profiling; cognitive science; network science; sentiment analysis; computational social science COVID-19; social media; hashtag networks; emotional profiling; cognitive science; network science; sentiment analysis; computational social science
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Figure 1

  • Externally hosted supplementary file 1
    Description: Dataset for the main manuscript about emotional profiling through cognitive network science and social media. The dataset includes the TweetIDs of Italian tweets related to COVID-19 and the Italian lockdown. These tweets had to include at least one of the following hashtags: #sciacalli, #italylockdown and #iorestoacasa.
MDPI and ACS Style

Stella, M.; Restocchi, V.; De Deyne, S. #lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19. Big Data Cogn. Comput. 2020, 4, 14.

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