# Detecting Pathogenic Social Media Accounts without Content or Network Structure

## TL;DR

This paper presents an unsupervised causality-based method that detects harmful social media accounts capable of spreading misinformation without relying on content, network, or user data, achieving higher precision than existing methods.

## Contribution

The authors introduce a novel unsupervised causality and label propagation framework that identifies pathogenic social media accounts without using traditional content or network features.

## Key findings

- Achieves 0.75 precision in detection
- Outperforms random and bot detection baselines
- Does not require content or network data

## 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.