  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.
