Evaluating Analysis of Different Machine Learning Models for Identification of Fake News

Abstract

As is clear from the arguments made earlier in this paper, the issue of fake news is rapidly becoming a major threat to the society. This research looks into different ML techniques to classify fake news in an attempt to overcome previous approaches’ deficits. Historically its effects have posed only relatively moderate threat, however as it has been established it is sufficiently dynamic phenomenon that requires more effective methods for efficiently combating it. Studies use all the synthetic and real news articles in its entirety with enhanced preprocessing techniques to ensure data credibility. We used varieties of models including conventional models such as Naive Bayes, Linear SVM as well as latest state of the art neural network models including LSTMs, GRUs and more complex architectures with multiple layers. Evidently, the work delivers the substantial improvement of the classical method and reaching accuracy of more than 96% using the custom models, based on the bidirectional LSTM and attention mechanism. This study contributes to the field by showing the application and effectiveness of deep learning approaches in detecting fake news in specific and offers basis for further studies to achieve better outcomes and enforcement of the values.