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Article

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

1
Complex Science Consulting, 73100 Lecce, Italy
2
School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK
3
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; https://doi.org/10.3390/bdcc4020014
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
    Link: https://osf.io/jy5kz/
    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. https://doi.org/10.3390/bdcc4020014

AMA Style

Stella M, Restocchi V, De Deyne S. #lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19. Big Data and Cognitive Computing. 2020; 4(2):14. https://doi.org/10.3390/bdcc4020014

Chicago/Turabian Style

Stella, Massimo, Valerio Restocchi, and Simon De Deyne. 2020. "#lockdown: Network-Enhanced Emotional Profiling in the Time of COVID-19" Big Data and Cognitive Computing 4, no. 2: 14. https://doi.org/10.3390/bdcc4020014

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