FAKE NEWS DETECTION SYSTEM

This paper efficiently implements a BERT-based model for identifying fake news that classifies news articles into real and fake categories using sophisticated methods of natural language processing and machine learning. The model has gone through all stages of development including data collection and integration, preprocessing, EDA, transformation with TF-IDF and BERT, model training, and evaluation. The experiment shows that the implemented BERT-based classifier performs significantly better than baseline ML models, achieving 95.8% accuracy and 95.7% F1 score. The context understanding capability of BERT was a decisive factor behind such an impressive result. Future improvements could involve metadata and social features, advanced transformers (RoBERTa, XLNet, GPT), larger training datasets, and deployment as a web or mobile application.

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