AI Based Fake News Detection Using Natural Language Processing (NLP)

Abstract—The rapid spread of fake news across digital plat-forms poses significant challenges to information reliability and public trust. To address this issue, this paper proposes a hybrid fake news detection framework that integrates statistical feature extraction techniques with transformer-based contextual repre-sentations. The proposed system combines TF-IDF features with BERT embeddings and employs a dual-stage classification strat-egy to achieve a balance between computational efficiency and classification accuracy. The framework processes textual data through preprocessing, feature extraction, and classification stages within a unified architecture. Experimental evaluation on a benchmark dataset demonstrates that the hybrid approach outperforms traditional machine learning and standalone deep learning models in terms of precision, recall, and F1-score. The results highlight the effectiveness of combining statistical and semantic representations for improving fake news detection in real-world scenarios. This work contributes to the development of scalable and efficient AI-based solutions for combating misinformation.

Keywords: Fake news detection, Natural Language Pro-cessing, Hybrid model, Transformer models, BERT, Machine learning, Misinformation