Detection of Misinformation Related to Pandemic Diseases Using Machine Learning

The advent of the COVID-19 pandemic has brought with it not only a global health crisis but also an infodemic characterized by the rampant spread of misinformation on social media platforms. In response to the urgent need for effective misinformation detection, this study presents a comprehensive approach harnessing machine learning and deep learning techniques, culminating in ensemble methods, to combat the proliferation of COVID-19 misinformation on Facebook, Twitter, Instagram, and YouTube. Drawing from a rich dataset comprising user comments on these platforms, encompassing diverse COVID-19-related discussions, our research applies SVM, decision tree, logistic regression, and neural networks to perform in-depth analysis and classification of comments into two categories: positive and negative information. The innovation of our approach lies in the final phase, where we employ ensemble methods to consolidate the strengths of various machine learning and deep learning algorithms. After applying ensemble learning, accuracy reached 91% for Facebook content, 79% for Instagram, 80% for Twitter, and 95% for YouTube.