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Area of Science:

  • Computational Linguistics
  • Public Health Informatics
  • Information Science

Background:

  • The COVID-19 pandemic saw a surge in online misinformation and disinformation, termed the 'infodemic'.
  • This infodemic poses significant public health risks, fueling false remedies, conspiracy theories, and xenophobia.
  • Combating health misinformation is a priority for global health organizations like the World Health Organization.

Purpose of the Study:

  • To develop and evaluate a novel method for combating the COVID-19 infodemic.
  • To enhance the credibility assessment of online health information.
  • To address challenges of data sparsity and class imbalance in misinformation detection.

Main Methods:

  • Proposed a prompt-based curriculum learning method for analyzing online social media texts.
  • The model verifies content reliability by answering a series of questions about the text.
  • Utilized prompt tuning and curriculum learning strategies for misinformation assessment.

Main Results:

  • The proposed method demonstrated effectiveness in assessing the reliability of COVID-19 related text.
  • Outperformed traditional text classification models such as fastText and BERT.
  • The approach showed robustness to hyperparameter settings, enhancing practical applicability.

Conclusions:

  • Prompt-based curriculum learning is a viable strategy for combating health misinformation.
  • The method offers a scalable solution for identifying and mitigating the societal harm of the infodemic.
  • This approach is particularly useful in resource-limited settings due to its robustness.