Hybrid Deep Learning Approach for Robust Fake News Detection

Detection of Fake News in the age where fake news gets a boom across digital platforms faster than wildfire is essential. In this study, an effective model to detect fake news is introduced that involves a fusion of advanced feature extraction techniques and complex deep learning models. It uses both: Count vectorization–The count of the frequency of occurrence for each word, TF-IDF weight to scale rare words. Instead it roots common and frequent tokens by their inherent importance. In order to improve the detection accuracy, this study merges Convolutional Neural Networks (CNN) with LSTM and BiLSTM and Artificial neural networks with LSTM and BiLSTM. At the other hand, it allows for studying linguistic nuances and contextual signals in news content through a combination of both shallow text analysis as well deep learning techniques. These results showed that this approach boost the classification performance by a large margin reducing all kinds of errors allowing at the same time high precision to Identify between what is fake and real news. This study is important to the global struggle against misinformation and demonstrates advanced deep learning can help retain veracity of information on digital platforms.