Title-Based Fake News Detection Using LSTM

In today’s information-driven society, the rapid dissemination of fake news through digital platforms has emerged as a critical issue, undermining public trust and distorting societal discourse. As online misinformation continues to spread, there is a growing demand for advanced automated techniques to effectively detect and combat false information. This study introduces a Long Short-Term Memory (LSTM) based model tailored for fake news detection, concentrating on the analysis of news titles. The model exploits the inherent temporal dependencies and contextual clues within headlines to accurately differentiate between authentic and fabricated news. Tested on a comprehensive dataset, the model achieved an impressive accuracy of 93.5%, with precision and recall rates reaching 92.8% and 94.2%, respectively. These findings demonstrate the efficacy of leveraging news titles in the detection of fake news and underscore the potential of LSTM networks as a robust tool in the ongoing battle against misinformation.