Detecting Pathogenic Social Media Accounts without Content or Network Structure

Abstract

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.

8 pages, 5 figures, International Conference on Data Intelligence and Security. arXiv admin note: text overlap with arXiv:1905.01553

References in corpus (2)

• Near linear time algorithm to detect community structures in large-scale networks
• The Temporal Logic of Causal Structures