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@article{176749,
        author = {Amrinder Pal Singh and Aditya Upadhyay},
        title = {Fake News Detection using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {11},
        pages = {8125-8127},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=176749},
        abstract = {The issue of fake news has far-reaching consequences, particularly in democratic societies were public opinion shapes political and social decision-making. The increased reliance on digital media necessitates advanced solutions that can proactively identify and mitigate misinformation. By leveraging AI and ML, we are not only enhancing technological responses to misinformation but also paving the way for safer information ecosystems.
The issue of fake news has far-reaching consequences, particularly in democratic societies were public opinion shapes political and social decision-making. The increased reliance on digital media necessitates advanced solutions that can proactively identify and mitigate misinformation. By leveraging AI and ML, we are not only enhancing technological responses to misinformation but also paving the way for safer information ecosystems.
The issue of fake news has far-reaching consequences, particularly in democratic societies were public opinion shapes political and social decision-making. The increased reliance on digital media necessitates advanced solutions that can proactively identify and mitigate misinformation. By leveraging AI and ML, we are not only enhancing technological responses to misinformation but also paving the way for safer information ecosystems.
With the widespread reach of the internet and social media platforms, fake news has become a significant challenge affecting public perception and social stability. This paper presents a robust system for detecting fake news using machine learning techniques. We propose a hybrid approach that combines text-based features, natural language processing, and machine learning algorithms such as Naïve Bayes and Support Vector Machines (SVM). Our system was tested on a labeled dataset and achieved an accuracy rate of 93.6%, outperforming several existing models. This research aims to provide a reliable solution to mitigate the impact of misinformation on digital platforms.},
        keywords = {Fake News, Machine Learning, Natural Language Processing, Naïve Bayes, SVM, Text Classification, Social Media},
        month = {June},
        }