Identifying Fake Digital Information Using Machine Learning Algorithms: Performance Analysis and Recommendation System

Abstract

This work focuses on the detection of fake digital information using various machine learning and deep learning algorithms to prevent its spread through Internet of Things (IoT) devices and systems. The research highlights the significance of detecting and preventing false or misleading information in critical areas such as healthcare, public safety, and emergency response. The study compares the performance of several supervised machine learning algorithms and identifies logistic regression as the most accurate (98.03%). The empirical analysis used data from The Indian Express, PolitiFact, and Kaggle and leveraged natural language processing (NLP) to prepare, clean, and model the data. To detect fraudulent posts, the study employed random forest, a supervised machine learning algorithm, which achieved an impressive accuracy rate of 99.71% on a Kaggle dataset. The research also developed a model for detecting false reporting related to COVID-19, utilizing the support vector machine technique, which achieved an accuracy rate of 78.69%. The presented work also determined the authenticity of images through convolutional neural networks (CNNs). Lastly, a content-based recommendation system was developed to enhance people’s security and confidence.