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Hybrid Deep Learning Models Based on Transformers for Fake News Detection in Albanian and English News
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 2 |
| Year of Publication: 2025 |
| Authors: Elton Tata, Jaumin Ajdari, Xhemal Zenuni, Mentor Hamiti |
10.5120/ijca2025924805
|
Elton Tata, Jaumin Ajdari, Xhemal Zenuni, Mentor Hamiti . Hybrid Deep Learning Models Based on Transformers for Fake News Detection in Albanian and English News. International Journal of Computer Applications. 187, 2 ( May 2025), 62-71. DOI=10.5120/ijca2025924805
Abstract
In the era of social media, news propagation has shifted to digital platforms enabling faster and more extensive information distribution. Fake News Detection (FND) has gained considerable attention, with various studies, but still, it remains a complex task. This study providesan approach that combines several deep learning architectures. First, the Albanian and IFND datasets are collected and loaded. Subsequently, the CoreNLP toolbox is utilized for data pre-processing and feature extraction, followed by the application of the RMS-BERT-CapsNet (Root Mean Square - Bidirectional Encoder Representations from Transformers - Capsule Network) framework. A Deep-Shallow multimodal fusion approach based on Variational Autoencoder (VAE) used to fit and encode the textual and visual data. Performance metrics such as accuracy, loss, F1 score, training loss, and validation loss are used to evaluate the efficiency of the proposed methodology.
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