Detecting Pathogenic Social Media Accounts without Content or Network Structure
This addresses the challenge of identifying malicious accounts without relying on content or network data, which is crucial for social media platforms, though it appears incremental as it builds on causality and label propagation.
The paper tackled the problem of detecting pathogenic social media accounts that spread harmful misinformation, achieving a precision of 0.75 compared to 0.11 for random and 0.16 for existing bot detection methods.
The spread of harmful mis-information in social media is a pressing problem. We refer accounts that have the capability of spreading such information to viral proportions as "Pathogenic Social Media" accounts. These accounts include terrorist supporters accounts, water armies, and fake news writers. We introduce an unsupervised causality-based framework that also leverages label propagation. This approach identifies these users without using network structure, cascade path information, content and user's information. We show our approach obtains higher precision (0.75) in identifying Pathogenic Social Media accounts in comparison with random (precision of 0.11) and existing bot detection (precision of 0.16) methods.