# An End-to-End Framework to Identify Pathogenic Social Media Accounts on Twitter

## TL;DR

This paper presents an end-to-end framework using causal inference and graph metrics to detect pathogenic social media accounts on Twitter quickly, achieving significant improvements over existing methods.

## Contribution

It introduces a novel causal inference-based approach for early detection of PSM accounts without relying on network or content analysis, enhancing detection accuracy.

## Key findings

- Achieves 0.28 higher F1 score than previous methods
- Attains 0.90 precision in identifying PSM accounts
- Demonstrates effectiveness on real-world Twitter data

## Abstract

Pathogenic Social Media (PSM) accounts such as terrorist supporter accounts and fake news writers have the capability of spreading disinformation to viral proportions. Early detection of PSM accounts is crucial as they are likely to be key users to make malicious information "viral". In this paper, we adopt the causal inference framework along with graph-based metrics in order to distinguish PSMs from normal users within a short time of their activities. We propose both supervised and semi-supervised approaches without taking the network information and content into account. Results on a real-world dataset from Twitter accentuates the advantage of our proposed frameworks. We show our approach achieves 0.28 improvement in F1 score over existing approaches with the precision of 0.90 and F1 score of 0.63.