Classifying and fact-checking health-related information about COVID-19 on Twitter/X using machine learning and deep learning models.

Journal: BMC medical informatics and decision making
PMID: 39934858

Abstract

BACKGROUND: Despite recent progress in misinformation detection methods, further investigation is required to develop more robust fact-checking models with particular consideration for the unique challenges of health information sharing. This study aimed to identify the most effective approach for detecting and classifying reliable information versus misinformation health content shared on Twitter/X related to COVID-19.

Authors

  • Elham Sharifpoor
    Medical Library and Information Sciences Department, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran.
  • Maryam Okhovati
    Medical Library and Information Sciences Department, Medical Informatics Research Center, Institute for Futures Studies in Health, Kerman University of Medical Sciences, Kerman, Iran. okhovati.maryam@gmail.com.
  • Mostafa Ghazizadeh-Ahsaee
    Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
  • Mina Avaz Beigi
    Department of Computer Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.

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