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Cluster-Based BERTopic Modeling on Swedish COVID-19 Vaccine Posts.

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Summary
This summary is machine-generated.

This study analyzed COVID-19 vaccine discussions on the Swedish forum Flashback, identifying key themes and user perspectives. The research offers insights into public discourse surrounding vaccination in Sweden.

Keywords:
BERTopicSwedish datasettopic modelingvaccinationvaccine

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Area of Science:

  • Social Sciences
  • Public Health
  • Computational Linguistics

Background:

  • Online forums like Flashback are significant platforms for public discourse.
  • Discussions surrounding COVID-19 vaccines and vaccination are prevalent globally and in Sweden.
  • Understanding public sentiment and prevalent themes is crucial for public health communication.

Purpose of the Study:

  • To explore prevalent themes in COVID-19 vaccine and vaccination discussions on the Swedish forum Flashback.
  • To distinguish between positive and negative perspectives within these discussions.
  • To provide nuanced insights into the multifaceted nature of public discourse on vaccination.

Main Methods:

  • Utilized BERTopic, a topic modeling framework leveraging pre-trained language models.
  • Applied clustering techniques to identify prevailing topics within 14 relevant discussion threads.
  • Analyzed posts to differentiate between positive and negative sentiment regarding vaccines.

Main Results:

  • Identified overarching themes and dominant topics in COVID-19 vaccine discussions.
  • Quantified and characterized positive and negative perspectives expressed by users.
  • Revealed the complexity and diversity of opinions on vaccination within the forum.

Conclusions:

  • The study provides valuable insights into the nature of online discussions about COVID-19 vaccines in Sweden.
  • Findings highlight the importance of monitoring public forums for understanding vaccine sentiment.
  • Results can inform targeted public health communication strategies regarding vaccination.