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Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy

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  • De Rosis, Sabina
  • Lopreite, Milena
  • Puliga, Michelangelo
  • Vainieri, Milena

Abstract

The novel coronavirus 2019 revolutionized the way of living and the communication of people making social media a popular tool to express concerns and perceptions. Starting from this context we built an original database based on the Twitter users’ emotions shown in the early weeks of the pandemic in Italy. Specifically, using a single index we measured the feelings of four groups of stakeholders (journalists, people, doctors, and politicians), in three groups of Italian regions (0,1,2), grouped according to the impact of the COVID-19 crises as defined by the Conte Government Ministerial Decree (8th March 2020). We then applied B-VAR techniques to analyze the sentiment relationships between the groups of stakeholders in every Region Groups. Results show a high influence of doctors at the beginning of the epidemic in the Group that includes most of Italian regions (Group 0), and in Lombardy that has been the region of Italy hit the most by the pandemic (Group 2). Our outcomes suggest that, given the role played by stakeholders and the COVID-19 magnitude, health policy interventions based on communication strategies may be used as best practices to develop regional mitigation plans for the containment and contrast of epidemiological emergencies.

Suggested Citation

  • De Rosis, Sabina & Lopreite, Milena & Puliga, Michelangelo & Vainieri, Milena, 2023. "Analyzing the emotional impact of COVID-19 with Twitter data: Lessons from a B-VAR analysis on Italy," Socio-Economic Planning Sciences, Elsevier, vol. 87(PB).
    • Handle: RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123001106
    DOI: 10.1016/j.seps.2023.101610
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    References listed on IDEAS

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    1. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    2. Hannah Brenkert‐Smith & Katherine L. Dickinson & Patricia A. Champ & Nicholas Flores, 2013. "Social Amplification of Wildfire Risk: The Role of Social Interactions and Information Sources," Risk Analysis, John Wiley & Sons, vol. 33(5), pages 800-817, May.
    3. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    4. De Rosis, Sabina & Lopreite, Milena & Puliga, Michelangelo & Vainieri, Milena, 2021. "The early weeks of the Italian Covid-19 outbreak: sentiment insights from a Twitter analysis," Health Policy, Elsevier, vol. 125(8), pages 987-994.
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