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 …

Metadata

publication

WWW '24: Proceedings of the ACM Web Conference 2024, 2530-2541, 2024

year

2024

publication date

2024/5/13

authors

Luca Luceri, Valeria Pantè, Keith Burghardt, Emilio Ferrara

link

https://dl.acm.org/doi/abs/10.1145/3589334.3645529

resource_link

https://arxiv.org/pdf/2310.09884

conference

WWW '24: Proceedings of the ACM Web Conference 2024

pages

2530-2541