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Combat COVID-19 infodemic using explainable natural language processing models.

Jackie Ayoub1, X Jessie Yang2, Feng Zhou1

  • 1Industrial and Manufacturing Systems Engineering, University of Michigan-Dearborn, 4901 Evergreen Road, Dearborn, MI 48128, United States of America.

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Summary
This summary is machine-generated.

This study introduces an explainable AI model using DistilBERT and SHAP to detect COVID-19 misinformation on social media, enhancing accuracy and public trust in AI predictions.

Keywords:
BERTCOVID-19DistilBERTMisinformation detectionSHAPTrust

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Social Science

Background:

  • The proliferation of COVID-19 misinformation on social media poses significant risks.
  • Deep learning models, particularly BERT, show promise in misinformation detection.
  • There is a need for efficient, effective, and explainable models to combat health misinformation.

Purpose of the Study:

  • To develop and evaluate an explainable natural language processing model for detecting COVID-19 misinformation.
  • To enhance public trust in AI-driven misinformation detection systems.
  • To assess the impact of explainability features on user trust and information sharing.

Main Methods:

  • A dataset of COVID-19 claims was collected and augmented using back-translation.
  • A DistilBERT model was trained for misinformation detection.
  • SHAP (Shapley Additive exPlanations) was employed to provide model explainability.
  • A between-subjects experiment evaluated user trust across different explanation conditions (Text, Text+SHAP, Text+SHAP+Source/Evidence).

Main Results:

  • The DistilBERT model achieved high performance in detecting COVID-19 misinformation (Accuracy: 0.972, AUC: 0.993 on the initial dataset; Accuracy: 0.938, AUC: 0.985 on a larger dataset).
  • The explainable model outperformed traditional machine learning approaches.
  • Participants showed significantly higher trust and willingness to share information when presented with SHAP explanations, especially when combined with source and evidence.

Conclusions:

  • The proposed explainable AI model is effective in detecting COVID-19 misinformation with high accuracy.
  • Incorporating SHAP explanations significantly boosts user trust and the perceived credibility of AI predictions.
  • This approach offers a promising strategy for combating health misinformation and fostering public confidence in AI technologies.