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@article{192843,
        author = {Manisha raja shinde and Samruddhi Vijay kale and Shravani Ganesh Rachcha and Prof. Sayyed H.A},
        title = {Fake-News-Detection-Using-Machine-Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {3407-3412},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192843},
        abstract = {The rapid expansion of social networking platforms has significantly increased the circulation of misleading and fabricated information. Fake news not only manipulates public perception but also affects political stability, financial markets, and public health awareness. This research proposes a hybrid deep learning framework that combines Bidirectional Long Short-Term Memory (BiLSTM) and Convolutional Neural Networks (CNN) for accurate fake news detection. The model leverages contextual word embeddings using Word2Vec and applies attention mechanisms to capture semantic relationships within news content. Experimental analysis demonstrates improved performance over traditional machine learning approaches, achieving superior accuracy and robustness. The proposed system is scalable and suitable for deployment in real-time verification systems.},
        keywords = {},
        month = {February},
        }