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Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy

Author

Listed:
  • Gabriela Fernandez

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA)

  • Carol Maione

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA
    Department of Management, Economics, and Industrial Engineering, Politecnico di Milano, 20156 Milan, Italy)

  • Harrison Yang

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA)

  • Karenina Zaballa

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA)

  • Norbert Bonnici

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA
    Malta Critical Infrastructure Protection Directorate, 1532 Valletta, Malta)

  • Jarai Carter

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA
    Smart Lab, Procter & Gamble, Champaign, IL 61820, USA)

  • Brian H. Spitzberg

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA
    Department of Communication, San Diego State University, San Diego, CA 92182, USA)

  • Chanwoo Jin

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA)

  • Ming-Hsiang Tsou

    (Metabolism of Cities Living Lab, Center for Human Dynamics in the Mobile Age, Department of Geography, San Diego State University, San Diego, CA 92182, USA)

Abstract

The pandemic spread rapidly across Italy, putting the region’s health system on the brink of collapse, and generating concern regarding the government’s capacity to respond to the needs of patients considering isolation measures. This study developed a sentiment analysis using millions of Twitter data during the first wave of the COVID-19 pandemic in 10 metropolitan cities in Italy’s (1) north: Milan, Venice, Turin, Bologna; (2) central: Florence, Rome; (3) south: Naples, Bari; and (4) islands: Palermo, Cagliari. Questions addressed are as follows: (1) How did tweet-related sentiments change over the course of the COVID-19 pandemic, and (2) How did sentiments change when lagged with policy shifts and/or specific events? Findings show an assortment of differences and connections across Twitter sentiments (fear, anger, and joy) based on policy measures and geographies during the COVID-19 pandemic. Results can be used by policy makers to quantify the satisfactory level of positive/negative acceptance of decision makers and identify important topics related to COVID-19 policy measures, which can be useful for imposing geographically varying lockdowns and protective measures using historical data.

Suggested Citation

  • Gabriela Fernandez & Carol Maione & Harrison Yang & Karenina Zaballa & Norbert Bonnici & Jarai Carter & Brian H. Spitzberg & Chanwoo Jin & Ming-Hsiang Tsou, 2022. "Social Network Analysis of COVID-19 Sentiments: 10 Metropolitan Cities in Italy," IJERPH, MDPI, vol. 19(13), pages 1-31, June.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:13:p:7720-:d:846199
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    References listed on IDEAS

    as
    1. Fan, Rui & Xu, Ke & Zhao, Jichang, 2018. "An agent-based model for emotion contagion and competition in online social media," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 245-259.
    2. Md Shoaib Ahmed & Tanjim Taharat Aurpa & Md Musfique Anwar, 2021. "Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
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    Cited by:

    1. Fernando Arias & Ariel Guerra-Adames & Maytee Zambrano & Efraín Quintero-Guerra & Nathalia Tejedor-Flores, 2022. "Analyzing Spanish-Language Public Sentiment in the Context of a Pandemic and Social Unrest: The Panama Case," IJERPH, MDPI, vol. 19(16), pages 1-19, August.

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