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Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social

Liang-Chin Huang1, Amanda L Eiden2, Long He1

  • 1Melax Tech, Houston, TX, United States.

JMIR Medical Informatics
|June 21, 2024
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Summary
This summary is machine-generated.

This study developed a real-time tool using natural language processing (NLP) to track vaccine sentiment and hesitancy on social media. The system analyzes millions of discussions to provide insights for public health campaigns.

Keywords:
NLPattitudeattitudesclassificationhesitancymachine learningnatural language processingopinionperceptionperceptionsperspectiveperspectivesreal-time trackingsentimentsentimentssocial mediasocial media platformsuptakevaccinationvaccinationsvaccinevaccine hesitancyvaccine sentimentvaccineswillingwillingness

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Area of Science:

  • Public Health Informatics
  • Computational Social Science
  • Natural Language Processing

Background:

  • Vaccine hesitancy is a major public health challenge impacting vaccine uptake.
  • Traditional survey methods for tracking hesitancy have limitations.
  • Real-time monitoring of public sentiment is crucial for timely interventions.

Purpose of the Study:

  • To develop a novel natural language processing (NLP) tool for real-time assessment of vaccine sentiment and hesitancy.
  • To analyze vaccine-related discussions across major social media platforms.
  • To create a dashboard for visualizing trends in vaccine sentiment and hesitancy.

Main Methods:

  • Collected and analyzed over 86 million English-language discussions from Twitter (X), Reddit, and YouTube (2011-2021).
  • Applied NLP algorithms to classify sentiment (positive, neutral, negative) and vaccine hesitancy using the WHO 3Cs model (confidence, complacency, convenience).
  • Developed an online dashboard to display and contextualize identified trends.

Main Results:

  • Top-performing NLP models achieved accuracies of 0.51-0.78 for sentiment and 0.69-0.91 for hesitancy classification.
  • Analysis revealed distinct patterns in online vaccine sentiment and hesitancy across different vaccine types.
  • The platform demonstrated variations in online activity related to vaccine sentiment and hesitancy.

Conclusions:

  • An innovative system was created for real-time analysis of vaccine sentiment and hesitancy on social media.
  • The tool provides crucial trend insights to support public health initiatives.
  • This approach aids campaigns focused on enhancing vaccine uptake and community health.