TL;DR
A transfer-cum-ensemble learning framework that integrates a task-specific pretrained model (XLM-RoBERTa) with a lightweight Indian-language model (IndicBERT) with a weighted-average attention mechanism is used to combine these sophisticated language models for further enhancing performance.
This study examines the effectiveness of two transformer-based architectures—BERT and DeBERTa—for identifying fake news using only textual information from headlines and article bodies and achieves strong performance on FakeDiverse corpus, demonstrating the need for enhanced generalization strategies as well as domain...
The proposed approach provides a simple and efficient solution for multilingual fake news detection in data-scarce environments with ensemble-based classifiers such as Random Forest and Gradient Boosting achieving reliable performance across both languages.
A hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset is proposed and Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models.
This article proposes a three-stage algorithm for training a model to recognize fake news in the Ukrainian language, using the multilingual transformer model XLM-RoBERTa, which solves this problem by utilizing cross-lingual knowledge transfer from English to Ukrainian.
The proposed framework highlights the potential of integrating transformer-based language models with classical machine learning algorithms to build robust and scalable fake news detection systems.
Disinformation on digital platforms is a very important problem for public trust and political debate. Even so, most research on automated fake news detection has stayed on a small number of high-resource languages. This paper presents AzFakeNews, the first large-scale benchmark dataset for fake news detection in Azerb...
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