A Comprehensive Approach to Fake News Detection with Adversarial Training and Model Explainability

Abstract

1 Abstract —The proliferation of fake news has become a significant challenge in the digital era, with misinformation spreading rapidly across online platforms and social media. This paper explores the application of natural language processing and machine learning techniques to automatically detect and classify fake news articles. Multiple classification models, including traditional algorithms and modern deep learning architectures, were trained and evaluated on a dedicated dataset. To analyze the robustness of these models, adversarial attacks were applied using the TextAttack framework. Such attacks simulate subtle modifications in the input text, exposing potential vulnerabilities that may lead to misclassifications. In addition, explainability techniques such as LIME were employed to interpret model predictions and to better understand the factors influencing decision-making. A web-based application was developed to integrate the trained models into an interactive platform. The system allows users to analyze news articles by either providing the full text or submitting a URL, while administrators have access to model explanations, database management, and retraining functionalities.