Fake News Detection on Social Media Using NLP

The spread of false information on social media sites creates serious problems for the accuracy of information and public confidence. Differentiating between real and fake information has grown more difficult as billions of people actively consume and share content online. The use of Natural Language Processing (NLP) approaches for automated fake news identification is investigated in this research. To categorize news as authentic or fraudulent, machine learning and deep learning models assess a variety of linguistic, semantic, and contextual factors. We assess several NLP-based models, such as Transformer-based architectures like BERT, Bidirectional LSTM, and TF-IDF using Logistic Regression. According to experimental findings, deep learning techniques—in particular, BERT-based models—achieve better accuracy and generalization across a variety of datasets. The paper explores future paths for enhancing explainability and cross-domain adaptability while highlighting the potential of NLP in reducing misinformation.