Enhancing Fake News Detection via Stance Analysis: Leveraging Advanced NLP Techniques and Machine Learning Models

Abstract

Fake news detection is still a field of research that is in its infancy, and this is clearly evident as it has only recently gained significant attention from society. The use of machine learning algorithms and natural language processing (NLP) techniques offers valuable problem-solving opportunities to address these complex challenges. This study explores stance detection as a method to identify misinformation by examining the connection between article headlines and their corresponding body text. Utilizing the FNC-1 and FARN datasets, we apply advanced NLP methods and machine learning (ML) models, including logistic regression, XGBoost, and DistilBERT. Key preprocessing techniques such as lemmatization, named entity recognition (NER), sentiment analysis, and semantic similarity are employed to capture both linguistic and contextual features. The experimental results show that transformer-based models such as DistilBERT achieve superior performance compared to traditional approaches, particularly in accurately classifying nuanced stances. These findings highlight the crucial role of context-aware models in improving the accuracy of misinformation detection and demonstrate their potential for scalable, real-world applications.