Identifying Coordinated Activities on Online Social Networks Using Contrast Pattern Mining

TL;DR

This paper introduces a novel framework using contrast pattern mining to detect coordinated malicious activities on social media by analyzing behavioral growth patterns over time.

Contribution

It proposes a new approach leveraging the EPClose algorithm to identify abnormal behavioral growth indicative of coordination, outperforming existing methods.

Findings

Minimum 10% increase in F1 score over existing approaches

Effective detection of coordinated malicious campaigns in real-world data

Demonstrates the utility of contrast pattern mining in social network analysis

Abstract

The proliferation of misinformation and disinformation on social media networks has become increasingly concerning. With a significant portion of the population using social media on a regular basis, there are growing efforts by malicious organizations to manipulate public opinion through coordinated campaigns. Current methods for identifying coordinated user accounts typically rely on either similarities in user behaviour, latent coordination in activity traces, or classification techniques. In our study, we propose a framework based on the hypothesis that coordinated users will demonstrate abnormal growth in their behavioural patterns over time relative to the wider population. Specifically, we utilize the EPClose algorithm to extract contrasting patterns of user behaviour during a time window of malicious activity, which we then compare to a historical time window. We evaluated the effectiveness of our approach using real-world data, and our results show a minimum increase of 10% in the F1 score compared to existing approaches.