Design and Implementation of Machine Learning Algorithms for the Detection of Misinformation on Social Media Platforms

Abstract

The proliferation of the internet and the rapid adoption of public news platforms, such as Facebook (FB), Twitter, and Instagram, have facilitated an unprecedented level of information dissemination in human history. Social media platforms enable users to create and share vast amounts of information, much of which is inaccurate or irrelevant to the discourse. Categorizing written content as misleading or disinformation algorithmically presents significant challenges. Even domain experts must consider multiple factors to determine the veracity of an item. To detect false news, researchers advocate using machine learning classification techniques. This study investigates various textual features that can distinguish between false and true content. We train multiple machine learning algorithms using diverse integrated approaches and evaluate their performance on real-world datasets. Our proposed ensemble learning method outperforms individual models.