Sitraka Herinambinina Andrianarisoa, Henri Michaël Ravelonjara, Geerish Suddul, Ravi Foogooa, Sandhya Armoogum, Doorgesh Sookarah

A Deep Learning Approach to Fake News Classification Using LSTM

Introduction

A deep learning approach to fake news classification using lstm. Combat misinformation with a deep learning approach to fake news classification. Our LSTM model achieves 0.9974 accuracy in detecting false information, a significant improvement.

Abstract

The rapid spread of misinformation on digital platforms poses a major challenge today. The ability to detect false information is essential to mitigate the associated harmful consequences. This research presents a deep learning approach for detecting fake news using Long Short-Term Memory (LSTM) model, which captures linguistic patterns and long-term dependencies in text. Our approach consists of optimizing the model through different experiments based on hyperparameter tuning, on a pre-processed dataset. The evaluation is performed using different metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the LSTM model achieves high accuracy of 0.9974, with embedding dimension of 128 using 100 LSTM units, batch size of 64 and drop-out rate of 0.48. It is a substantial improvement over previous studies. The application of cross-validation further confirms the model’s reliability. This research demonstrates that the application of a fine-tuned LSTM network with robust data preprocessing can provide a powerful tool to combat online misinformation.

Review

This paper presents a deep learning approach for fake news classification using Long Short-Term Memory (LSTM) networks, aiming to address the critical societal challenge of misinformation spread. The authors highlight their method's ability to capture linguistic patterns and long-term dependencies in text, optimized through extensive hyperparameter tuning on a pre-processed dataset. The abstract reports impressively high performance metrics, including an accuracy of 0.9974, and claims a "substantial improvement over previous studies," with the model's reliability further supported by cross-validation. The proposed fine-tuned LSTM network is presented as a powerful tool to combat online misinformation. While the reported performance is striking, several critical aspects require further clarification to fully assess the work's contribution and robustness. An accuracy of 0.9974 in fake news classification is exceptionally high for a real-world problem and immediately raises questions regarding the nature and complexity of the dataset used. Essential details such as the dataset's size, source, how fake and real news instances were defined and collected, and its balance are missing. Without this context, it is difficult to ascertain whether the high performance truly reflects robust real-world applicability or if it might be influenced by factors like data leakage, a simplistic dataset, or potential overfitting. Furthermore, the abstract mentions "previous studies" that this work substantially improves upon, but it does not specify which baselines were used for comparison, making it challenging to contextualize the claimed improvement. To significantly strengthen the paper and justify its claims, the authors must provide a much more detailed and transparent account of their experimental setup. This includes a comprehensive description of the dataset, explicit comparisons against a range of contemporary and state-of-the-art baselines (e.g., transformer-based models) on the same dataset, and a more thorough analysis of the specific preprocessing steps. Addressing the concerns regarding the exceptionally high accuracy through a detailed discussion of potential limitations, dataset characteristics, and error analysis will be crucial for the work to be considered a robust and significant contribution to the field of fake news detection.

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