Robust Detection of Fake News Using LSTM and GloVe Embeddings

Abstract

The pervasive issue of fake news on digital platforms poses a significant threat to public opinion and trust in media. This paper addresses the problem of fake news detection by leveraging advanced natural language processing (NLP) techniques and deep learning models. Utilizing the ISOT Fake News Dataset, which comprises balanced samples of verified and fake news articles, we develop and evaluate two primary models: a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). The LSTM model employs pre-trained GloVe embeddings, followed by LSTM layers and a fully connected layer for classification, while the CNN model incorporates convolutional layers, max-pooling, and dropout for comparative analysis. Extensive pre-processing and exploratory data analysis (EDA) were conducted to clean the data and understand its characteristics. Our results demonstrate that the LSTM model outperforms the CNN model, achieving an accuracy of 99.58% on the test set. However, the high performance raises concerns about dataset biases, suggesting the need for more diverse and challenging datasets to ensure model robustness. Future work will focus on adversarial training and explainability techniques to enhance the model’s resilience and interpretability.