Classification of unlabeled online media

This work investigates the ability to classify misinformation in online social media networks in a manner that avoids the need forground truth labels. Rather than approach the classification problem as a task for humans or machine learning algorithms, thiswork leverages user-user and user-media (i.e.,media likes) interactions to infer the type of information (fake vs. authentic) beingspread, without needing to know the actual details of the information itself. To study the inception and evolution of user-userand user-media interactions over time, we create an experimental platform that mimics the functionality of real world socialmedia networks. We develop a graphical model that considers the evolution of this network topology to model the uncertainty(entropy) propagation when fake and authentic media disseminates across the network. The creation of a real-world socialmedia network enables a wide range of hypotheses to be tested pertaining to users, their interactions with other users, andwith media content. The discovery that the entropy of user-user, and user-media interactions approximates fake and authenticmedia likes, enables us to classify fake media in an unsupervised learning manner.