Early Identification of Pathogenic Social Media Accounts

TL;DR

This paper introduces a causal inference-based method for early detection of pathogenic social media accounts using only initial user activity logs, achieving high precision within 10 days.

Contribution

It presents a novel time-decay causality metric and a causal community detection algorithm for early PSM identification without relying on network or content data.

Findings

Achieved 0.84 precision using only first 10 days of activity

Effective detection of PSMs within a short time frame

Method outperforms existing techniques in early detection

Abstract

Pathogenic Social Media (PSM) accounts such as terrorist supporters exploit large communities of supporters for conducting attacks on social media. Early detection of these accounts is crucial as they are high likely to be key users in making a harmful message "viral". In this paper, we make the first attempt on utilizing causal inference to identify PSMs within a short time frame around their activity. We propose a time-decay causality metric and incorporate it into a causal community detection-based algorithm. The proposed algorithm is applied to groups of accounts sharing similar causality features and is followed by a classification algorithm to classify accounts as PSM or not. Unlike existing techniques that take significant time to collect information such as network, cascade path, or content, our scheme relies solely on action log of users. Results on a real-world dataset from Twitter demonstrate effectiveness and efficiency of our approach. We achieved precision of 0.84 for detecting PSMs only based on their first 10 days of activity; the misclassified accounts were then detected 10 days later.