Using Causality to Infer Coordinated Attacks in Social Media

TL;DR

This paper presents a novel causality-based method using Convergent Cross Mapping to detect coordinated attacks on social media, demonstrating effectiveness on real-world datasets including election interference and COVID-19 discussions.

Contribution

The work introduces a causality inference approach with topic modeling to improve detection of coordinated social media attacks, advancing beyond existing thematic or network-based methods.

Findings

Achieved up to 75.3% F1 score in identifying coordinated accounts

Effectively uncovered causal structures in social media attack campaigns

Demonstrated applicability on real-world datasets like election interference and COVID-19 discussions

Abstract

The rise of social media has been accompanied by a dark side with the ease of creating fake accounts and disseminating misinformation through coordinated attacks. Existing methods to identify such attacks often rely on thematic similarities or network-based approaches, overlooking the intricate causal relationships that underlie coordinated actions. This work introduces a novel approach for detecting coordinated attacks using Convergent Cross Mapping (CCM), a technique that infers causality from temporal relationships between user activity. We build on the theoretical framework of CCM by incorporating topic modelling as a basis for further optimizing its performance. We apply CCM to real-world data from the infamous IRA attack on US elections, achieving F1 scores up to 75.3% in identifying coordinated accounts. Furthermore, we analyse the output of our model to identify the most influential users in a community. We apply our model to a case study involving COVID-19 anti-vax related discussions on Twitter. Our results demonstrate the effectiveness of our model in uncovering causal structures of coordinated behaviour, offering a promising avenue for mitigating the threat of malicious campaigns on social media platforms.