Document Type : Methodologies
Authors
Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran.
10.22044/jadm.2025.15646.2681
Abstract
The widespread use of web-based forums and social media has led to an increase in news consumption. To mitigate the impact of misinformation on users' health-related decisions, it is crucial to develop machine learning models that can automatically detect and combat fake news. In this paper, we propose a novel multilingual model with dynamic transformer model called Hybrid CapsNet for Covid-19 fake news detection in English and Persian languages. Our model incorporates two dynamic pre-trained representation models that incrementally uptrain and update the word embeddings in the training phase., dynamic RoBERTa for English and dynamic ParsBERT for Persian, and two parallel classifiers with new loss function namely margin loss. By utilizing dynamic transformer and both Deep Convolutional Neural Networks (DCNN) and Capsule Neural Networks (CapsNet), we achieve better performance than state-of-the-art baselines. To evaluate the proposed model, we use two recent Covid-19 datasets in English and Persian. Our results, in terms of F1-score, demonstrate the effectiveness of the Hybrid CapsNet model. Our model outperforms existing baselines, suggesting that it can be an effective tool for detecting and combating fake news related to Covid-19 in multiple languages. Overall, our study highlights the importance of developing effective machine learning models for combating misinformation during critical events such as the Covid-19 pandemic. The proposed model has the potential to be applied to other languages and domains and can be a valuable tool for protecting public health and safety.
Keywords
- Fake News Detection
- Covid-19
- dynamic transformer
- Deep Convolutional Neural Networks
- Capsule Neural Networks
Main Subjects
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