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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: Jul 2, 2024
Date Accepted: May 14, 2025

The final, peer-reviewed published version of this preprint can be found here:

Themes, Policies, and Attention Shifts Regarding COVID-19 Vaccinations in German-Speaking Regions: Infoveillance Study Using Tweets

Boland K, Starke C, Bensmann F, Marcinkowski F, Dietze S

Themes, Policies, and Attention Shifts Regarding COVID-19 Vaccinations in German-Speaking Regions: Infoveillance Study Using Tweets

J Med Internet Res 2025;27:e63909

DOI: 10.2196/63909

PMID: 41172291

PMCID: 12619015

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

A computational analysis of German online discourses about COVID-19 vaccinations to inform policy-making in times of crisis

  • Katarina Boland; 
  • Christopher Starke; 
  • Felix Bensmann; 
  • Frank Marcinkowski; 
  • Stefan Dietze

ABSTRACT

Background:

Societies worldwide have witnessed growing rifts separating advocates and opponents of vaccinations and other COVID-19 countermeasures. With the rollout of vaccination campaigns, the German-speaking DACH region (Germany, Austria, Switzerland), initially exhibited a noticeably low vaccination uptake compared to other European regions. Later, uptake increased. It remains unclear which factors contributed to these changes.

Objective:

This study aims to shed light on the intricacies of vaccine hesitancy among the German-speaking population and the possible dynamics between policy changes and public concerns using online discourse data. These insights are valuable for policy-makers tasked with making far-reaching decisions: policies need to effectively curb the spread of the virus and at the same time respect fundamental civic liberties and minimize undesired consequences.

Methods:

This study draws on data from X , formerly Twitter. We use a hybrid pipeline to detect and analyze 191,750 German vaccination-related tweets using a semi-automatic seed list generation approach, topic modeling, sentiment analysis, and a minimum of social scientific domain knowledge to analyze the discourse about vaccinations in the light of the COVID-19 pandemic. We further analyze the evolution of public attention during different phases of the pandemic and in relation to policy changes to identify potential drivers of shifts in public attention.

Results:

We find that skepticism regarding the severity of the COVID-19 virus, towards efficacy and safety of vaccines and other medical concerns were among the prevalent topics in the discourse on Twitter but that the most attention was given to debating the theme of freedom and civic liberties. During the later phases of the pandemic, policies were implemented that restricted the freedom of unvaccinated citizens. During these phases, increased vaccination uptake could be observed, and the attention increasingly shifted from medical and other concerns towards questions of freedom and civic liberties. Yet, increasingly negative and polarized sentiments were expressed.

Conclusions:

Our analyses suggest potential interactions between policies, public attention to different topics and associated sentiments. While vaccination uptake increased, our findings indicate that citizens' doubts and concerns did not decrease. This study showcases the use of analyzing online discourse data for data-driven policy-making in highly dynamic contexts such as the COVID-19 pandemic where monitoring the discourse can serve to provide insights on suitable actions to properly and timely respond to citizens' concerns.


 Citation

Please cite as:

Boland K, Starke C, Bensmann F, Marcinkowski F, Dietze S

Themes, Policies, and Attention Shifts Regarding COVID-19 Vaccinations in German-Speaking Regions: Infoveillance Study Using Tweets

J Med Internet Res 2025;27:e63909

DOI: 10.2196/63909

PMID: 41172291

PMCID: 12619015

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