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This study analyzed public sentiment during the COVID-19 pandemic from 2020-2022. It reveals long-term shifts in public health discourse, offering insights for future crisis communication.

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Area of Science:

  • Public Health
  • Communication Studies
  • Computational Social Science

Background:

  • The COVID-19 pandemic presented unprecedented public health challenges.
  • Effective crisis communication is vital during large-scale health emergencies.
  • Previous research often analyzed public sentiment in discrete timeframes.

Purpose of the Study:

  • To conduct a longitudinal sentiment analysis of public discourse related to the COVID-19 pandemic.
  • To identify long-term patterns and evolution of public sentiment from 2020 to 2022.
  • To offer insights for enhancing future public health crisis communication strategies.

Main Methods:

  • Longitudinal analysis of public health discourse.
  • Sentiment analysis utilizing natural language processing (NLP) techniques.
  • Temporal pattern analysis to identify discourse transition points.

Main Results:

  • Identified key transition points in public health discourse and sentiment over a three-year period (2020-2022).
  • Revealed long-term patterns in the evolution of public sentiment regarding the pandemic.
  • Demonstrated the utility of NLP and temporal analysis in understanding crisis communication dynamics.

Conclusions:

  • Longitudinal analysis provides a deeper understanding of public sentiment shifts during prolonged health crises.
  • Identifying discourse transition points can inform more adaptive and effective crisis communication.
  • This study contributes valuable data for public health organizations and communication strategists.