Detecting Coordinated Activities Through Temporal, Multiplex, and Collaborative Analysis

• URL: http://arxiv.org/abs/2512.19677v1
• Date: Mon, 22 Dec 2025 18:53:43 GMT
• Title: Detecting Coordinated Activities Through Temporal, Multiplex, and Collaborative Analysis
• Authors: Letizia Iannucci, Elisa Muratore, Antonis Matakos, Mikko Kivelä,
• Abstract summary: coordinated campaigns are better characterized by evidence of similar temporal behavioral patterns.<n>We propose a framework to model complex coordination patterns across multiple online modalities.<n>Our results demonstrate that a multiplex time-aware model excels in the identification of coordinating groups.
• Score: 2.7415651415305597
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In the era of widespread online content consumption, effective detection of coordinated efforts is crucial for mitigating potential threats arising from information manipulation. Despite advances in isolating inauthentic and automated actors, the actions of individual accounts involved in influence campaigns may not stand out as anomalous if analyzed independently of the coordinated group. Given the collaborative nature of information operations, coordinated campaigns are better characterized by evidence of similar temporal behavioral patterns that extend beyond coincidental synchronicity across a group of accounts. We propose a framework to model complex coordination patterns across multiple online modalities. This framework utilizes multiplex networks to first decompose online activities into different interaction layers, and subsequently aggregate evidence of online coordination across the layers. In addition, we propose a time-aware collaboration model to capture patterns of online coordination for each modality. The proposed time-aware model builds upon the node-normalized collaboration model and accounts for repetitions of coordinated actions over different time intervals by employing an exponential decay temporal kernel. We validate our approach on multiple datasets featuring different coordinated activities. Our results demonstrate that a multiplex time-aware model excels in the identification of coordinating groups, outperforming previously proposed methods in coordinated activity detection.

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