A General Method to Find Highly Coordinating Communities in Social Media through Inferred Interaction Links

TL;DR

This paper introduces a novel temporal window-based method to detect highly coordinating communities in social media, revealing hidden networks of malicious or goal-oriented groups through interaction and metadata analysis.

Contribution

It presents a new approach combining temporal windowing, network inference, and community detection to identify coordinated groups in social media, applicable in near real-time.

Findings

Effective detection of coordinated communities validated on real datasets.

Outperforms existing methods in identifying hidden networks.

Demonstrated utility in analyzing contentious online discussions.

Abstract

Political misinformation, astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Many previous approaches examining these phenomena have focused on broad campaigns rather than the small groups responsible for instigating or sustaining them. To reveal latent (i.e., hidden) networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in various behaviours that, in concert, come to execute different goal-based strategies, a number of which we describe. The approach relies upon a pipeline that extracts relevant elements from social media posts, infers connections between accounts based on criteria matching the coordination strategies to build an undirected weighted network of accounts, which is then mined for communities exhibiting high levels of evidence of coordination using a novel community extraction method. We address the temporal aspect of the data by using a windowing mechanism, which may be suitable for near real-time application. We further highlight consistent coordination with a sliding frame across multiple windows and application of a decay factor. Our approach is compared with other recent similar processing approaches and community detection methods and is validated against two relevant datasets with ground truth data, using content, temporal, and network analyses, as well as with the design, training and application of three one-class classifiers built using the ground truth; its utility is furthermore demonstrated in two case studies of contentious online discussions.