Fake News Detection Approach Using Hybrid Deep Learning Framework

The rapid expansion of social media has facilitated individuals’ widespread distribution of information, frequently without sufficient verification or filtering mechanisms. This has exacerbated the problem of misinformation, raising significant concerns about the harmful impacts of fake news on communities. To counteract this issue, a focused effort has been made to develop automated detection systems that harness artificial intelligence (AI) and machine learning (ML) capabilities. Given the success of deep learning (DL) techniques in managing complex natural language processing (NLP) tasks, these methods offer a promising avenue for detecting fake news. This study introduces an innovative DL framework that effectively combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to detect fake news. The proposed approach attains 99.19% accuracy and significantly superior detection outcomes compared to existing methods, highlighting its potential to significantly enhance the reliability of news verification processes and mitigate the spread of misinformation.