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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
Summary
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.
- 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.
