FAKE NEWS DETECTION AND SOURCE CREDIBILITY ANALYSIS USING TRANSFORMER-BASED NLP MODELS
DOI:
https://doi.org/10.29121/shodhkosh.v6.i4s.2025.6946Keywords:
Fake News Detection, Source Credibility Analysis, Transformer Models, Natural Language Processing, BERT, ROBERTA, XLNET, Explainable AI, Multitask Learning, Misinformation PreventionAbstract [English]
Because false information is spread so easily on digital platforms, we need strong ways to find fake news and check the reliability of sources. To deal with these problems, this study suggests a complete system that uses advanced transformer-based Natural Language Processing (NLP) models. Using the contextual understanding features of frameworks like BERT, RoBERTa, and XLNet, the system accurately tells whether news stories are true or false while also checking the credibility of their sources. The method uses multi-task learning to improve both the classification of fake news and the reliability score, which improves the total accuracy of the predictions. To choose data that is good for deep contextual embedding, a lot of preparation is done, such as tokenisation, normalisation, and entity recognition. The model is trained and tested on freely available datasets that cover a wide range of topics and levels of reliability. This makes sure that it can be used in a wide range of fields. To fully judge how well the model works, evaluation measures like accuracy, F1-score, and Area under the Curve (AUC) are used. Also, AI methods that can be explained are used to make predictions clear, which makes them easier for end users to trust and understand. The test results show that transformer-based models are much better at finding fake news and judging the reliability of a source than standard machine learning baselines. At the end of the study, possible real-world uses and social issues related to automated truth-checking methods are talked about. In the future, researchers will likely look into how to make the framework more useful in global settings by adding language features and real-time recognition systems.
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Copyright (c) 2025 Somanath Sahoo, Dr. Ganesh Baliram Dongre, Naman Soni, Avinash Somatkar, Mukul Pande, Dr. Shikha Dubey
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