Navigating the Newscape: Long Short-Term Memory Models for Misinformation Detection

In an era where digital misinformation poses a severe challenge to societal trust, identifying fake news is imperative. This study employs a Long Short-Term Memory (LSTM) neural network to discern misinformation within a dataset of approximately 79,000 labeled news texts. Our LSTM model benefits from its aptitude for processing sequential data, crucial for understanding textual context. We evaluate our model's proficiency through accuracy, precision, recall, and F1 score, key metrics reflecting its classification capability. The model exhibits robust performance with a test accuracy of 94.09%, precision of 91.57%, recall of 95.45%, and an F1 score of 93.47%. These results underscore the potential of LSTM networks in automating fake news detection, which could significantly support the integrity of information consumption in the digital realm.