Unmasking the Web of Deceit: Uncovering Coordinated Activity to Expose Information Operations on Twitter

TL;DR

This paper presents a new framework combining network analysis and machine learning to accurately detect and predict key actors involved in coordinated influence operations on Twitter, using a large-scale dataset.

Contribution

It introduces a node pruning-based framework and a supervised machine learning model that outperform traditional methods in identifying IO drivers across campaigns.

Findings

Supervised model achieves over 95% precision in classifying IO drivers.

Combining multiple behavioral indicators improves detection accuracy.

Traditional network filtering methods are less effective across different campaigns.

Abstract

Social media platforms, particularly Twitter, have become pivotal arenas for influence campaigns, often orchestrated by state-sponsored information operations (IOs). This paper delves into the detection of key players driving IOs by employing similarity graphs constructed from behavioral pattern data. We unveil that well-known, yet underutilized network properties can help accurately identify coordinated IO drivers. Drawing from a comprehensive dataset of 49 million tweets from six countries, which includes multiple verified IOs, our study reveals that traditional network filtering techniques do not consistently pinpoint IO drivers across campaigns. We first propose a framework based on node pruning that emerges superior, particularly when combining multiple behavioral indicators across different networks. Then, we introduce a supervised machine learning model that harnesses a vector representation of the fused similarity network. This model, which boasts a precision exceeding 0.95, adeptly classifies IO drivers on a global scale and reliably forecasts their temporal engagements. Our findings are crucial in the fight against deceptive influence campaigns on social media, helping us better understand and detect them.