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[Sentiments in the COVID-19 crisis communication of German authorities and independent experts on Twitter : A sentiment analysis for the first year of the pandemic]. / Emotionalität in der COVID-19-Krisenkommunikation von Behörden und unabhängigen Expert*innen auf Twitter : Eine Sentiment-Analyse für das erste Pandemiejahr.
Drescher, Larissa S; Roosen, Jutta; Aue, Katja; Dressel, Kerstin; Schär, Wiebke; Götz, Anne.
  • Drescher LS; C³ team GbR, Zennerstr. 13, 81379, München, Deutschland.
  • Roosen J; C³ team GbR, Zennerstr. 13, 81379, München, Deutschland. jroosen@tum.de.
  • Aue K; TUM School of Management, Lehrstuhl für Marketing und Konsumforschung, Technische Universität München, Alte Akademie 16, 85354, Freising, Deutschland. jroosen@tum.de.
  • Dressel K; C³ team GbR, Zennerstr. 13, 81379, München, Deutschland.
  • Schär W; Süddeutsches Institut für empirische Sozialforschung e. V., Schwanthalerstr. 91, 80336, München, Deutschland.
  • Götz A; Süddeutsches Institut für empirische Sozialforschung e. V., Schwanthalerstr. 91, 80336, München, Deutschland.
Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz ; 66(6): 689-699, 2023 Jun.
Artículo en Alemán | MEDLINE | ID: covidwho-2322843
ABSTRACT

BACKGROUND:

At the beginning of the COVID­19 pandemic in Germany, there was great uncertainty among the population and among those responsible for crisis communication. A substantial part of the communication from experts and the responsible authorities took place on social media, especially on Twitter. The positive, negative, and neutral sentiments (emotions) conveyed there during crisis communication have not yet been comparatively studied for Germany. STUDY

AIM:

Sentiments in Twitter messages from various (health) authorities and independent experts on COVID­19 will be evaluated for the first pandemic year (1 January 2020 to 15 January 2021) to provide a knowledge base for improving future crisis communication. MATERIAL AND

METHODS:

From n = 39 Twitter actors (21 authorities and 18 experts), n = 8251 tweets were included in the analysis. The sentiment analysis was done using the so-called lexicon approach, a method within the social media analytics framework to detect sentiments. Descriptive statistics were calculated to determine, among other things, the average polarity of sentiments and the frequencies of positive and negative words in the three phases of the pandemic. RESULTS AND

DISCUSSION:

The development of emotionality in COVID­19 tweets and the number of new infections in Germany run roughly parallel. The analysis shows that the polarity of sentiments is negative on average for both groups of actors. Experts tweet significantly more negatively about COVID­19 than authorities during the study period. Authorities communicate close to the neutrality line in the second phase, that is, neither distinctly positive nor negative.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Medios de Comunicación Sociales / COVID-19 Tipo de estudio: Estudio experimental / Estudio observacional / Ensayo controlado aleatorizado Límite: Humanos País/Región como asunto: Europa Idioma: Alemán Revista: Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz Asunto de la revista: Salud Pública Año: 2023 Tipo del documento: Artículo

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Medios de Comunicación Sociales / COVID-19 Tipo de estudio: Estudio experimental / Estudio observacional / Ensayo controlado aleatorizado Límite: Humanos País/Región como asunto: Europa Idioma: Alemán Revista: Bundesgesundheitsblatt Gesundheitsforschung Gesundheitsschutz Asunto de la revista: Salud Pública Año: 2023 Tipo del documento: Artículo