Forge News Detection: Random Forest and Bi- LSTM-based Hybrid Approach

Fake news and its detection are now a critical issue in the world that needs to be solved as its propagation has many consequences. Because of social media's low cost and simple accessibility, as well as the quick dissemination of information, it attracts more individuals to read news from it which can't be identified whether it's fake or real. As a result, automating the identification of false information has become critical to maintaining a stable news source online and on social media. Various algorithms are used to detect and eradicate forged news. Detection methods had several limitations that degraded their performance. In this article, an improved hybrid approach is proposed combining the tree-based ML and RNN-based Bi-LSTM techniques which purely enhance the system accuracy, and a comparison of the performance matrix of the state-of-the-art research had presented.