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@article{201051,
        author = {Mrs. B. Lakshmidevi and Jansirani T and Madhavi B and Sivasakthi E},
        title = {Multilingual Fake News Detection Using Multimodal Transformers and Semi-Supervised Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {265-270},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201051},
        abstract = {Fake news has emerged as a serious global issue due to the rapid spread of misinformation through social media and online platforms. Recent research indicates that deep learning models, particularly transformer-based architectures, outperform traditional machine learning methods in fake news detection . However, challenges remain in multilingual detection, multimodal data handling, limited labeled datasets, and explainability.This project proposes a Multilingual Fake News Detection system that integrates Multimodal Transformers with Semi-Supervised Machine Learning. The system analyzes textual and visual content simultaneously and leverages unlabeled data to improve model generalization. The proposed framework enhances accuracy, scalability, and adaptability while reducing dependency on large labeled datasets. Experimental results demonstrate improved classification performance compared to traditional supervised approaches.},
        keywords = {Fake News Detection, Multilingual NLP, Multimodal Learning, Transformers, Semi-Supervised Learning, Deep Learning.},
        month = {May},
        }