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This survey consolidates the state of the art in detecting coordinated online behavior, which relies heavily on machine learning and graph analysis techniques to distinguish between legitimate community organization and malicious manipulation campaigns. By reconciling industry and academic definitions, it establishes a critical framework for applying AI-driven detection methods to combat disinformation and hate speech in social networks. The work highlights key challenges in characterizing these behaviors, offering essential guidance for researchers developing next-generation algorithms for online safety.
Detection and Characterization of Coordinated Online Behavior: A Survey
arXiv:2408.01257v3 Announce Type: replace-cross 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. We also provide an interactive companion website for exploring the surveyed literature.
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. We also provide an interactive companion website for exploring the surveyed literature.
Preprint version of an article published in ACM Computing Surveys. Please cite the published version: doi:https://doi.org/10.1145/3839225
Social and Information Networks (cs.SI); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
arXiv:2408.01257 [cs.SI]
(or arXiv:2408.01257v3 [cs.SI] for this version)
ACM Computing Surveys, Vol. 58, No. 16, Article 401 (2026)
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