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COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment-Based Topic Modeling.

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This study analyzed over 850,000 tweets about COVID-19 vaccines, revealing that positive sentiment focused on vaccination plans and information. Negative sentiment highlighted vaccine hesitancy and side effect concerns, with trends varying over time.

Keywords:
COVID-19COVID-19 vaccinesentiment evolutionsocial mediatext miningtopic modeling

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Area of Science:

  • Public Health
  • Social Media Analytics
  • Vaccinology

Background:

  • COVID-19 vaccines are crucial for pandemic control.
  • Understanding public perception is vital for effective vaccination campaigns.
  • Limited empirical research exists on public COVID-19 vaccine awareness trends via sentiment analysis.

Purpose of the Study:

  • To explore public sentiment and topics related to COVID-19 vaccines on Twitter.
  • To analyze the evolution of public attitudes and concerns following vaccine rollout.
  • To identify key themes in positive and negative discussions surrounding vaccines (Pfizer, Moderna, AstraZeneca, Johnson & Johnson).

Main Methods:

  • Collected 1,122,139 tweets from December 2020 to April 2021.
  • Filtered data to 857,128 unique tweets, excluding retweets.
  • Applied sentiment-based topic modeling using compound and coherence scores to analyze tweet content and sentiment evolution.

Main Results:

  • Positive tweets (46.51%) centered on vaccination planning and information.
  • Negative tweets (23.81%) focused on vaccine hesitancy and side effect concerns.
  • Negative sentiment remained stable, while positive sentiment fluctuated; topic trends evolved throughout the vaccination campaign.

Conclusions:

  • This study pioneers sentiment-based topic modeling to track public COVID-19 vaccine awareness on social media.
  • Findings offer valuable insights for policymakers and researchers to enhance vaccination strategies.
  • Results aid in understanding and addressing public concerns to promote vaccine uptake.