The rapid growth of social media platforms has significantly increased the spread of misinformation, posing serious challenges to information credibility and public trust. Due to the massive volume of online content, manual verification is impractical, making automated fake news detection essential. This review paper presents a comprehensive analysis of Natural Language Processing (NLP) techniques used for fake news detection. It examines both traditional machine learning models, such as TF-IDF with Logistic Regression and Passive Aggressive Classifier, as well as advanced deep learning approaches including BiLSTM and BERT. The findings indicate that transformer-based models, particularly BERT, achieve superior performance due to their contextual understanding, while lightweight models offer efficiency for real-time applications. Despite these advancements, challenges such as model interpretability, domain adaptation, and multilingual detection remain open for future research.

This review highlights the effectiveness of Natural Language Processing techniques in detecting fake news on social media platforms. Among various approaches, transformer-based models such as BERT demonstrate superior performance due to their ability to capture contextual and semantic information. Traditional machine learning models, although less accurate, remain useful for lightweight and real-time applications. The study emphasizes that combining syntactic and semantic features enhances detection capabilities. Future research should focus on multimodal analysis, improving model interpretability, and developing efficient models for real-time and multilingual environments.

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