Authors: Bangar Piyush Rajesh, Prof. S. K. Said, Prof. M. C. Kolse
Abstract: Social media, websites and online portals have become major sources of news, but they also allow false and misleading information to spread rapidly, creating confusion and social harm. Fake news detection aims to identify such content automatically so that users and platforms can respond quickly. This paper reviews machine learning and natural language processing approaches to detecting fake news from the text of articles and headlines. It covers how fake news is defined and why it spreads, benchmark datasets such as LIAR, ISOT and FakeNewsNet, preprocessing steps including cleaning, tokenization and stop-word removal, feature extraction with bag-of-words, n-grams and TF-IDF, and classification with Logistic Regression, Naive Bayes and Support Vector Machines, as well as deep models based on BERT. A practical five-layer pipeline that can be implemented with Python libraries such as Pandas, NumPy, Scikit-learn and NLTK is proposed, together with research gaps, objectives and a comparison of algorithms. Limitations of text-only detection, dataset bias and the need for explainable, regularly updated models are discussed. The review concludes that a well-built text-classification pipeline is an effective first line of defence and a strong foundation for richer detection systems.
DOI: https://doi.org/10.5281/zenodo.24220040
