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@article{191822,
        author = {Balaji Ambaldhage and Shaik Saifuddin and J Sai Charan and Shaik Lalmatti Abdul Gafur and Dr CV Madhusudan Reddy},
        title = {Fake news detection on twitter using text and video content},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {8016-8021},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=191822},
        abstract = {Everything from content creation to distribution and consumption has been affected by the meteoric rise of social media. On the other hand, the proliferation of false news has been accelerated by the digital revolution, which poses significant risks to public confidence, political stability, and social consciousness. This research introduces a deep learning framework that can identify false news stories by combining visual and linguistic data found in social media posts. When it comes to text representation, the system uses NLP techniques like TF-IDF and Word2Vec. When it comes to visual feature extraction, it uses CNNs like VGG16 and ResNet50. By combining the retrieved features, a complete representation is created that can capture the semantic and contextual interactions between images and text. The next step is to determine whether news articles are authentic using a Dense Neural Network (DNN) classifier. When tested experimentally on benchmark datasets, the suggested model outperforms the state-of-the-art text-only methods in terms of accuracy and robustness. According to the findings, the system's capacity to detect altered or deceptive content on social media sites is improved when visual and textual signals are combined.},
        keywords = {Fake news detection, multi-modal learning, deep learning, social media, TF-IDF, social media, CNN, feature fusion, authenticity verification, misinformation detection.},
        month = {January},
        }