Fake News Identification: An Effective Combined Approach using ML and DL Techniques

Abstract

Fake news refers to misleading or fake information spread over the internet or other communication networks. In our paper, we use different machine learning (ML) models and deep learning (DL) models for classifying news as fake or real. The different ML models used are k-nearest neighbor (KNN), random forest (RF), logistic regression, naive Bayes, and DL models like long short-term memory (LSTM), and gated recurrent units (GRU) for prediction. We developed a mechanism that combines the prediction probabilities of ML models and DL models for prediction. We achieved accuracy as high as 0.98 and F1 scores as high as 0.98 using our approach. We also analyze the results of classification using different graphs which give us meaningful insights into the accuracy of the prediction of different models. We use flow charts to demonstrate the flow of our proposed algorithm in the classification of news. The superiority of our model is demonstrated in experimental results.