Detection and Characterization of Coordinated Online Behavior: A Survey

TL;DR

This survey tackles coordinated online behavior by reconciling platform- and academic-definitions, and proposing a general framework built on three components—actors, actions, and intent—augmented by four defining dimensions: authenticity, harmfulness, orchestration, and time-variance. It systematically reviews detection and characterization methods, contrasting network-science approaches with data mining and machine-learning techniques, and highlights the benefits of multiplex and compound-action modeling for capturing complex coordination. The authors identify key challenges, including cross-platform and multimodal analyses, data scarcity, and the rising influence of generative AI, and they propose directions such as scalable, ethically aware, multilateral datasets and multidisciplinary collaboration. The work thus provides a roadmap for researchers, platforms, and policymakers to analyze, understand, and mitigate online coordination's intricate dynamics and real-world impact.

Abstract

Coordination is a fundamental aspect of life. The advent of social media has made it integral also to online human interactions, such as those that characterize thriving online communities and social movements. At the same time, coordination is also core to effective disinformation, manipulation, and hate campaigns. This survey collects, categorizes, and critically discusses the body of work produced as a result of the growing interest on coordinated online behavior. We reconcile industry and academic definitions, propose a comprehensive framework to study coordinated online behavior, and review and critically discuss the existing detection and characterization methods. Our analysis identifies open challenges and promising directions of research, serving as a guide for scholars, practitioners, and policymakers in understanding and addressing the complexities inherent to online coordination.

Figures (8)

• Figure 1: Coordination is a fundamental aspect of online human interactions and the study of coordinated online behavior can complement the analyses of many other online phenomena.
• Figure 2: Number of articles published yearly on coordinated online behavior. A steep rise is observed after Facebook introduced CIB in 2018.
• Figure 3: Taxonomy of coordinated online behavior obtained by considering the dimensions of harmfulness and authenticity of our conceptual framework. The framework conveniently allows the mapping of disparate instances of online coordination.
• Figure 4: The analytical process of studying coordinated online behavior, involving the detection and characterization tasks. The input to the overall process is a set of users $U$ and their activities $H$ on one or more platforms. The output of the detection task is either a set of binary labels $B$, clusters $C$, or network communities $G$ that differentiate coordinated and non-coordinated users. The characterization task receives these in input and outputs a set of indicators $M$.
• Figure 5: Main steps of the network science methods for the detection of coordinated online behavior. 1: The selected users become nodes in a network. 2: User similarities are computed with a similarity function and assigned to the edge weights of the network. 3: The network is filtered so as to retain only similarities with given properties. 4: Community discovery is performed to detect groups of strongly coordinated users.