Detecting and Analyzing Deceptive Information in News Articles: A Study Using a Dataset of Misleading Content

Abstract

Due to the widespread growth of social and traditional media in the last decade has led to the huge spread of information. Nowadays these platforms are playing a vital role in everyone’s life. Many people use these platforms to share and discuss different ideas and issues related to almost every field. Through the internet, getting the most recent news at their fingers has become simpler. However, in this dissemination of information, it has been noted that there is some misinformation and fake news circulating with no relevance to reality which can cause serious problems in political and social aspects. The main goal of this study is to find the ideal learning model for achieving high accuracy and performance. In this regard, this study offers a state-of-the-art dataset which is the extension of fake and real news datasets called the Misleading Content dataset. To analyze the performance of the dataset three different machine learning algorithms, Random Forest, Naïve Bayes (NB) and Support Vector Machine (SVM), and three different deep learning, Bidirectional Long Short-Term Memory (BiLSTM), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) models are being used. BiLSTM performs well and provides promising results along with 88.54% accuracy.