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      Firms’ challenges and social responsibilities during Covid-19: A Twitter analysis

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          Abstract

          This paper offers insights on the major issues and challenges firms face in the Covid-19 pandemic and their concerns for Corporate Social Responsibility (CSR) themes. To do so, we investigate large Italian firms’ discussions on Twitter in the first nine months of the pandemic. Specifically, we ask: How is firms’ Twitter discussion developing during the Covid-19 pandemic? Which CSR dimensions and topics do firms discuss? To what extent do they resonate with the public? We downloaded Twitter posts by the accounts of large Italian firms, and we built the bipartite network of accounts and hashtags. Using an entropy-based null model as a benchmark, we projected the information contained in the network into the accounts layers, identifying a network of accounts. We find that the network is composed of 13 communities and accounts at the core of the network focus on environmental sustainability, digital innovation, and safety. Firms’ ownership type does not seem to influence the conversation. While the relevance of CSR hashtags and stakeholder engagement is relatively small, peculiarities arise in some communities. Overall, our paper highlights the contribution of online social networks and complex networks methods for management and strategy research, showing the role of online social media in understanding firms’ issues, challenges, and responsibilities, with common narratives naturally emerging from data.

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            Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008
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              The COVID-19 social media infodemic

              We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document} R 0 for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors’ amplification.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Writing – original draftRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                2021
                27 July 2021
                27 July 2021
                : 16
                : 7
                : e0254748
                Affiliations
                [1 ] IMT School for Advanced Studies, Lucca, Italy
                [2 ] DSMN & ECLT, Università Ca’ Foscari, Mestre (Ve), Italy
                [3 ] CNR - Institute of Complex Systems, Unit “Sapienza”, Rome, Italy
                The Bucharest University of Economic Studies, ROMANIA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-1723-4511
                Article
                PONE-D-21-08077
                10.1371/journal.pone.0254748
                8315506
                34314432
                ab9cc2c3-bf74-4ef3-9803-e37f03941dfa
                © 2021 Patuelli et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 11 March 2021
                : 3 July 2021
                Page count
                Figures: 13, Tables: 0, Pages: 30
                Funding
                Funded by: EU project SoBigData
                Award ID: 871042
                Award Recipient :
                Funded by: PAI (Progetto di Attività Integrata) project TOFFEe
                Award Recipient :
                Funded by: EU grant Humane-AI-net
                Award ID: 952026
                Award Recipient :
                GC acknowledges support from the EU grant “Humane-AI-net” (Grant nr. 952026). GC and FS acknowledge support from EU project SoBigData++ (Grant nr. 871042); PAI (Progetto di Attività Integrata) project TOFFEe, funded by IMT School or Advanced Studies Lucca.
                Categories
                Research Article
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Twitter
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Twitter
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Twitter
                Medicine and Health Sciences
                Medical Conditions
                Infectious Diseases
                Viral Diseases
                Covid 19
                Medicine and Health Sciences
                Epidemiology
                Pandemics
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Media
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Media
                Social Sciences
                Sociology
                Social Networks
                Social Media
                Social Sciences
                Economics
                Finance
                Computer and Information Sciences
                Network Analysis
                Social Networks
                Social Sciences
                Sociology
                Social Networks
                Social Sciences
                Sociology
                Communications
                Social Communication
                Social Sciences
                Economics
                Custom metadata
                Twitter ID data can be downloaded from the website of the TOFFEe project at https://toffee.imtlucca.it/datasets. The firms’ ID and financial data that support the findings of this study are available from AIDA (Bureau Van Dijk). Restrictions apply to the availability of these data, which were used under license for this study.
                COVID-19

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