Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{201954,
        author = {Taksh Joshi and Arpita Prajapati},
        title = {Fake news detection using machine learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {5767-5773},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201954},
        abstract = {Fake news dissemination on digital platforms has become a significant challenge, affecting public opinion and societal stability. This paper presents a machine learning-based approach for detecting fake news using natural language processing techniques. The proposed system utilizes text preprocessing methods including tokenization, stop-word removal, and stemming, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). Multiple classification algorithms such as Naïve Bayes, Support Vector Machine (SVM), and Logistic Regression are implemented and compared to evaluate their performance in fake news detection. The models are trained and tested on publicly available datasets to ensure reliability and generalization. Experimental results indicate that the SVM classifier achieves higher accuracy compared to other models, demonstrating its effectiveness in handling high-dimensional textual data. The study also highlights the importance of feature engineering and balanced datasets in improving classification performance. The proposed approach can assist in automated content verification and help mitigate the spread of misinformation on social media platforms. Future work will focus on integrating deep learning models and real-time detection systems.},
        keywords = {Fake News Detection, Machine Learning, Natural Language Processing, TF-IDF, Classification},
        month = {May},
        }