Fake News Detection using LSTM and Random Forest

Onlinemisinformation is rapidly growing, and effective fake news detection mechanisms are needed. The main difficulty is in precisely detecting misleading news stories, which tend to appear similar to true news in format and content. This research meets this challenge by introducing a new hybrid model combining Long Short-Term Memory (LSTM) networks and Random Forest classifiers for enhanced detection precision. The LSTM network learns temporal dependencies and semantic characteristics in text data, and the Random Forest classifier improves decision-making via ensemble learning. Our model attained 96.63% accuracy on a real-world dataset, outperforming existing methods significantly. The proposed method fills the research gap well by integrating sequential pattern recognition with stable classification, making it a scalable method for fake news detection. Comparative evaluation indicates that our model outperforms the current methods in accuracy, precision, and recall. This work is an addition to the continued efforts in countering misinformation through offering a more accurate and robust detection framework.