Fake News Classifier: Advancements In Natural Language Processing For Automated Fact-Checking

Abstract

The proliferation of fake news presents significant challenges to information integrity, necessitating the development of advanced automated fact-checking systems. This study explores the role of Natural Language Processing (NLP) in enhancing fake news classification by reviewing 21 case studies that examine the effectiveness of various detection methodologies. Transformer-based models, including BERT, RoBERTa, and GPT, have demonstrated superior accuracy in misinformation detection, outperforming traditional lexicon-based and rule-based approaches. Additionally, the study highlights the impact of retrieval-based fact-checking, which improves claim verification by cross-referencing information with external knowledge bases. Multimodal approaches, integrating text and visual analysis, further enhance fake news detection by identifying inconsistencies in manipulated images and deepfake videos. Graph Neural Networks (GNNs) have been found to effectively analyze misinformation propagation patterns, providing deeper insights into how deceptive content spreads across digital platforms. However, the study also identifies key challenges, including dataset biases, adversarial misinformation tactics, and the computational demands of deep learning models. Explainable AI (XAI) has emerged as a critical solution to improve transparency and trust in automated misinformation detection, but trade-offs between interpretability and model accuracy remain a concern. The findings emphasize the necessity of a multi-faceted approach that integrates NLP, retrieval-based techniques, multimodal analysis, and network-based misinformation tracking to develop more effective and scalable fact-checking systems. This study contributes to the ongoing discourse on combating digital misinformation by providing a comprehensive analysis of state-of-the-art methodologies and highlighting areas for further research and improvement.