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@article{194748,
        author = {Umamaheswararao Mogili},
        title = {A Machine Learning Driven Approach for Detecting and Classifying Misinformation in Digital News Media},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {5866-5872},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194748},
        abstract = {The rapid expansion of digital news platforms and social media has led to a significant increase in the spread of fake news, which negatively impacts public opinion and social harmony. Manual verification of news content is time-consuming and inefficient. This paper presents an automated fake news detection system using machine learning and natural language processing (NLP) techniques. The proposed system preprocesses news text using tokenization, stop-word removal, and normalization, followed by feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF). Multiple machine learning classifiers, including Logistic Regression, Naive Bayes, Support Vector Machine, and Random Forest, are trained and evaluated. Experimental results demonstrate that the proposed approach achieves high accuracy and effectively distinguishes fake news from real news, making it suitable for real-world applications. With the increasing use of the internet and social networking sites, the amount of information shared online has grown rapidly. Among this information, fake news has become a major challenge because it spreads quickly and misleads people. Manual verification of news articles is time-consuming and not always practical due to the large volume of online content. This project proposes an automated fake news detection system using machine learning techniques. The system collects news articles from a dataset and processes the textual data using Natural Language Processing techniques. Feature extraction is performed using TF-IDF, which converts textual information into numerical vectors. Machine learning algorithms such as Naïve Bayes and Random Forest are trained on labeled datasets containing fake and real news articles. The trained model then predicts whether a new article is genuine or fake. The proposed system provides a fast and efficient way to verify online information and helps in controlling the spread of misinformation.},
        keywords = {},
        month = {March},
        }