A Deep Learning Approach to Fake News Classification Using LSTM - Sitraka Herinambinina Andrianarisoa; Henri Michaël Ravelonjara; Geerish Suddul; Ravi Foogooa; Sandhya Armoogum; Doorgesh Sookarah (Vokasi UNESA Bulletin of Engineering, Technology and Applied Science, 2025)
The rapid spread of misinformation on digital platforms poses a major significant challenge today. The ability to detect false information is essential to mitigate the crucial in mitigating its associated harmful consequences. This research presents a deep learning approach for detecting fake news using a Long Short-Term Memory (LSTM) model, which captures linguistic patternsand long-term dependencies in text. Our approach consists of involves optimizing the model through different various experiments based on hyperparameter tuning, using utilizing a pre-processed preprocessed dataset. The evaluation is performed using different metrics such as accuracy, precision, recall, and F1-score. Researchers evaluate the model using various metrics, including accuracy, precision, recall, and F1-score. Experimental results show that the LSTM model achieves a high accuracy of 0.9974, with an embedding dimension of 128 using, 100 LSTM units, a batch size of 64, and a dropout rate of 0.48. It is a substantial improvement over previous studies study. The application of cross-validation further confirms the model’s reliability. This research demonstrates that the application of a finetuned LSTM network, combined with robust data preprocessing, can provide a powerful tool to combat for combating online misinformation.