Evidence map›Paper›PMID 37193861›Full record

ArticleBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2023

[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].

Larissa S Drescher, Jutta Roosen, Katja Aue, Kerstin Dressel, Wiebke Schär, Anne Götz

Open access · hybridAbstract readEnglish Abstract
In one paragraph

Article in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.4field-weighted citation impact, top 17% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. Enhancing risk and crisis communication with computational methods: A systematic literature review.Risk analysis : an official publication of the Society for Risk Analysis · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Larissa S DrescherC³ team GbR, Zennerstr. 13, 81379, München, Deutschland.
Jutta RoosenC³ team GbR, Zennerstr. 13, 81379, München, Deutschland. jroosen@tum.de.
Katja AueC³ team GbR, Zennerstr. 13, 81379, München, Deutschland.
Kerstin DresselSüddeutsches Institut für empirische Sozialforschung e. V., Schwanthalerstr. 91, 80336, München, Deutschland.
Wiebke SchärSüddeutsches Institut für empirische Sozialforschung e. V., Schwanthalerstr. 91, 80336, München, Deutschland.
Anne GötzSüddeutsches Institut für empirische Sozialforschung e. V., Schwanthalerstr. 91, 80336, München, Deutschland.
Sine Institute · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAt the beginning of the COVID‑19 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

aimSentiments in Twitter messages from various (health) authorities and independent experts on COVID‑19 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

methodsFrom 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 COVID‑19 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 COVID‑19 than authorities during the study period. Authorities communicate close to the neutrality line in the second phase, that is, neither distinctly positive nor negative.

Indexed as

COVID-19Social MediaAttitudeCommunicationGermanyHumansPandemicsSentiment AnalysisAuthorities communicationExperts communicationsPublic RelationsSARS-CoV-2SentimentsSocial Media Analytics

Identifiers

PMID37193861
PMCPMC10187495
OpenAlexW4376871683

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.