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      Public Sphere in Crisis Mode: How the COVID-19 Pandemic Influenced Public Discourse and User Behaviour in the Swiss Twitter-sphere

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      , ,
      Javnost (Ljubljana, Slovenia)
      Routledge
      COVID-19, social media, Twitter, crisis communication, public sphere

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          Abstract

          In modern democracies, large societal crises like the COVID-19 pandemic are accompanied by intensified public discourse about which policies and strategies are adequate to fight the crisis. In such times, the public sphere switches to crisis mode with fundamentally different communicative dynamics compared to routinised periods. Data from social media platforms like Twitter offers new possibilities to study such dynamics. However, comprehensive studies on how crises affect discourse in distinct national publics are missing up to now. Based on 1,762,262 tweets referring to COVID-19 written between 1 January and 30 April 2020 by 56,418 validated Swiss users, we illustrate how the lockdown of public life in Switzerland affected the discourse in the Swiss Twitter-sphere. Based on public sphere theories, we identify four crisis-related dimensions for our analysis. We show that the pandemic led to a narrowing of the topic agenda and to a more inwardly oriented public sphere with increased Twitter activity by experts. Furthermore, actors from the social periphery were able to reach the centre of public discourse with their tweets. Overall our study shows how methodological innovation allows us to better connect an empirical analysis with the concept of a public sphere as a communication network.

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          MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

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            Maps of random walks on complex networks reveal community structure

            To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.
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              Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter

              Background Since the beginning of the coronavirus disease 2019 (COVID-19) epidemic, misinformation has been spreading uninhibited over traditional and social media at a rapid pace. We sought to analyze the magnitude of misinformation that is being spread on Twitter (Twitter, Inc., San Francisco, CA) regarding the coronavirus epidemic.  Materials and methods We conducted a search on Twitter using 14 different trending hashtags and keywords related to the COVID-19 epidemic. We then summarized and assessed individual tweets for misinformation in comparison to verified and peer-reviewed resources. Descriptive statistics were used to compare terms and hashtags, and to identify individual tweets and account characteristics. Results The study included 673 tweets. Most tweets were posted by informal individuals/groups (66%), and 129 (19.2%) belonged to verified Twitter accounts. The majority of included tweets contained serious content (91.2%); 548 tweets (81.4%) included genuine information pertaining to the COVID-19 epidemic. Around 70% of the tweets tackled medical/public health information, while the others were pertaining to sociopolitical and financial factors. In total, 153 tweets (24.8%) included misinformation, and 107 (17.4%) included unverifiable information regarding the COVID-19 epidemic. The rate of misinformation was higher among informal individual/group accounts (33.8%, p: <0.001). Tweets from unverified Twitter accounts contained more misinformation (31.0% vs 12.6% for verified accounts, p: <0.001). Tweets from healthcare/public health accounts had the lowest rate of unverifiable information (12.3%, p: 0.04). The number of likes and retweets per tweet was not associated with a difference in either false or unverifiable content. The keyword “COVID-19” had the lowest rate of misinformation and unverifiable information, while the keywords “#2019_ncov” and “Corona” were associated with the highest amount of misinformation and unverifiable content respectively. Conclusions Medical misinformation and unverifiable content pertaining to the global COVID-19 epidemic are being propagated at an alarming rate on social media. We provide an early quantification of the magnitude of misinformation spread and highlight the importance of early interventions in order to curb this phenomenon that endangers public safety at a time when awareness and appropriate preventive actions are paramount.
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                Author and article information

                Journal
                Javnost
                Javnost
                Javnost (Ljubljana, Slovenia)
                Routledge
                1318-3222
                1854-8377
                26 May 2021
                2021
                : 28
                : 2 , Reclaiming the Public Sphere in a Global Health Crisis, Guest Edited by Hans-Jörg Trenz, Annett Heft, Michael Vaughan and Barbara Pfetsch
                : 129-148
                Author information
                https://orcid.org/0000-0003-1232-083X
                https://orcid.org/0000-0002-0211-7574
                https://orcid.org/0000-0002-4964-2528
                Article
                1923622
                10.1080/13183222.2021.1923622
                8336575
                34393592
                8fec6674-e4dd-4d83-9e4c-8450f596f869
                © 2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group

                This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License ( http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

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                Figures: 2, Tables: 1, Equations: 0, References: 44, Pages: 20
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                Articles

                covid-19,social media,twitter,crisis communication,public sphere

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