标题： 多模态协同在线行为：权衡与策略 显示英文标题

标题： Multimodal Coordinated Online Behavior: Trade-offs and Strategies

摘要： 协调的在线行为，从有益的集体行动到有害的操纵，如虚假信息运动，已成为数字生态系统分析的关键焦点。 传统方法通常依赖于单一模式的方法，专注于诸如共同转发或共同标签等单一类型的互动，或者独立地考虑多种模式。 然而，这些方法可能会忽略多模式协调中的复杂动态。 本研究比较了操作化检测多模式协调行为的不同方法。 它考察了弱整合和强整合多模式模型之间的权衡，突出了捕捉更广泛的协调模式与识别紧密协调行为之间的平衡。 通过比较单一模式和多模式方法，我们评估了不同数据模式的独特贡献，并探讨了多模式的不同实现如何影响检测结果。 我们的研究结果表明，并非所有模式都提供独特的见解，但通过多模式方法，我们可以对协调动态获得更全面的理解。 这项工作增强了检测和分析协调在线行为的能力，为保障数字平台的完整性提供了新的视角。 显示英文摘要

摘要： Coordinated online behavior, which spans from beneficial collective actions to harmful manipulation such as disinformation campaigns, has become a key focus in digital ecosystem analysis. Traditional methods often rely on monomodal approaches, focusing on single types of interactions like co-retweets or co-hashtags, or consider multiple modalities independently of each other. However, these approaches may overlook the complex dynamics inherent in multimodal coordination. This study compares different ways of operationalizing the detection of multimodal coordinated behavior. It examines the trade-off between weakly and strongly integrated multimodal models, highlighting the balance between capturing broader coordination patterns and identifying tightly coordinated behavior. By comparing monomodal and multimodal approaches, we assess the unique contributions of different data modalities and explore how varying implementations of multimodality impact detection outcomes. Our findings reveal that not all the modalities provide distinct insights, but that with a multimodal approach we can get a more comprehensive understanding of coordination dynamics. This work enhances the ability to detect and analyze coordinated online behavior, offering new perspectives for safeguarding the integrity of digital platforms.