Authors: Jananai A, Nivetha D, Prabhavathi U, Sumathi S
Abstract: The rapid circulation of news through websites and social-media platforms makes it difficult for readers to distinguish trustworthy reporting from fabricated or misleading content. This paper presents an AI-based fake news detection system that applies Natural Language Processing (NLP) and machine learning to classify textual news content into predefined categories such as fake and real. The workflow includes data preparation, text cleaning, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), model training, prediction, and presentation of the result through an application interface. Logistic Regression is considered as the baseline classifier because it is efficient for sparse text features and provides a practical reference for text-classification tasks. The proposed system is intended to support early screening of suspicious news, not to replace source verification or professional fact-checking. The paper describes the design, implementation workflow, evaluation metrics, risks, and future improvements. Since verified experimental logs and final test measurements were not supplied for this manuscript, numerical performance is not fabricated; the results section specifies the measurements that the team should insert after running the final model. The work provides a structured, reproducible approach for building and evaluating a news-text classification application.
