Privacy Data Propagation and Preservation in Social Media: A Real-World Case Study

Social media has become a ubiquitous tool for spreading news, messages, and generally allowing for communication between individuals. Hence, studying how our privacy information might also spread across social media is important research. To date, many studies have used information diffusion models to simulate and then examine how information flows through social networks. But these models are theoretical, and newsworthy information may not behave in the same way as privacy information, raising the question: Are the observed phenomena indicative of real privacy propagation? To explore this question, we assembled a dataset from Twitter comprising propagated information flows for both private and normal information. We then built a graph convolutional network to trace and classify differences in the way each type of information spreads throughout the platform. The results reveal that there are indeed key differences in the diffusion processes of the two types of information. More importantly, we design privacy-preserving methods to reduce the privacy propagation in social media.