A Contrasting Evaluation of Deep learning and Machine learning Approaches for Identifying the Existence of Fake Health News

Abstract

Abstract: The issue of fake news, which was present even before Internet penetration, has been made worse by the growth and penetration of the internet. If there is news concerning health, this becomes even more concerning. This study suggests using feature-based models (FBM) and content-based models (CBM) to address this problem. The input given determines how the two models differ from one another. While the FBM also accepts two readability features as input in addition to content, the CBM only accepts news content. Two hybrid Deep Learning approaches, CNN-LSTM and CNN-BiLSTM, are compared with the performance of five traditional machine learning techniques, under each category: Decision Tree, Random Forest, Support Vector Machine, AdaBoost-Decision Tree, and AdaBoost-Random Forest.