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.

翻译：在在线内容广泛传播的时代，有效检测协同行为对于缓解信息操纵带来的潜在威胁至关重要。尽管在识别虚假账户和自动化行为者方面已取得进展，但若脱离协同群体单独分析，影响力活动中单个账户的行为可能不会显现出异常特征。鉴于信息操作的协作本质，协同活动更适宜通过超越偶然同步性的群体相似时序行为模式来表征。我们提出一个框架来建模跨多种在线模态的复杂协同模式。该框架首先利用多重网络将在线活动分解至不同的交互层，随后聚合各层中的在线协同证据。此外，我们提出一种时序感知协作模型，用以捕捉每种模态的在线协同模式。该时序感知模型基于节点归一化协作模型构建，并通过引入指数衰减时间核函数来处理不同时间间隔内协同动作的重复性。我们在包含多种协同活动的数据集上验证了所提方法。结果表明，多重时序感知模型在识别协同群体方面表现卓越，其性能优于先前提出的协同活动检测方法。