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@article{206009,
        author = {Tirth N Patel and Dr. Narayan Joshi},
        title = {Detecting Fake News Using an Ensemble of Machine Learning Models},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {9382-9389},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206009},
        abstract = {The fast spread of information via digital channels has revolutionized news consumption, but it has also accelerated the spread of fake news—misleading or fabricated content designed to manipulate audiences. To ensure that the students receive accurate information, detection of fake news becomes crucial. The main aim of this project is to explore different machine-learning based approaches to automate the detection of fake news using text classification. We are using three machine-learning models namely Logistic Regression (LR), Random Forest Classifier and Gradient Boosting Classifier (GBC) and applied Term Frequency-Inverse Document Frequency (TF-IDF). The dataset consists of Real and fake news which are equally divided among 25,000 articles. The true news was taken from Reuters.com and fake news were taken from unreliable websites flagged by Wikipedia and Politifact. The dataset consists of political and global news content published between 2016 and 2017. This research seeks to develop a scalable approach for detecting fake news using natural language processing (NLP) and ensemble learning methods.},
        keywords = {Fake News Identification, Machine Learning, TF-IDF, Logistic Regression, Random Forest, Gradient Boosting, NLP, Text Classification.},
        month = {June},
        }