Harnessing Large Language Models and Deep Neural Networks for Fake News Detection

The spread of fake news threatens trust in both traditional and digital media. Early detection methods, based on linguistic patterns and handcrafted features, struggle to identify more sophisticated misinformation. Large language models (LLMs) offer promising solutions by capturing complex text patterns, but challenges remain in ensuring their accuracy and generalizability. This study evaluates LLM-based feature extraction for fake news detection across multiple datasets. We compare BERT-based text representations, introduce a method for extracting factual segments from news articles, and create two new datasets with fact-based features. Additionally, we explore graph-based text representations using LLMs to capture relationships within news content. By integrating these approaches, we improve fake news detection, making it more accurate and interpretable. Our findings provide insights into how LLMs and graph-based techniques can enhance misinformation detection.