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An Optimized Hybrid Deep Learning Model to Detect COVID-19 Misleading Information.

Bader Alouffi1, Abdullah Alharbi2, Radhya Sahal3,4

  • 1Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia.

Computational Intelligence and Neuroscience
|November 18, 2021
PubMed
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This summary is machine-generated.

Detecting COVID-19 fake news is crucial. A new hybrid deep learning model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) shows superior performance in identifying misinformation.

Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Fake news detection is complex due to the amalgamation of accurate and inaccurate information from diverse sources.
  • Social media platforms pose significant challenges to information veracity, particularly during critical events like the COVID-19 pandemic.
  • The widespread dissemination of COVID-19 misinformation necessitates effective early detection strategies.

Purpose of the Study:

  • To propose and evaluate a novel hybrid deep learning model for the early detection of COVID-19 fake news.
  • To assess the efficacy of the proposed model against established machine learning and deep learning techniques.

Main Methods:

  • A hybrid deep learning architecture integrating Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) layers was developed.

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  • The model incorporates an embedding layer, convolutional layer, pooling layer, LSTM layer, flatten layer, dense layer, and an output layer.
  • Performance was evaluated using three COVID-19 fake news datasets and validated with accuracy, precision, recall, and F1-measure metrics.

    • Main Results:

    • The proposed hybrid CNN-LSTM model demonstrated superior performance compared to six traditional machine learning models (DT, KNN, LR, RF, SVM, NB) and two individual deep learning models (CNN, LSTM).
    • Experimental results confirmed the model's effectiveness across multiple validation metrics.
    • The system achieved significant capabilities in detecting COVID-19 related fake news.

    Conclusions:

    • The developed hybrid deep learning model offers a robust and effective solution for identifying COVID-19 fake news.
    • The proposed approach significantly outperforms existing methods, highlighting its potential for real-world application in combating health misinformation.
    • Early detection of fake news using advanced AI techniques is vital for mitigating its societal impact.