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 multimodal coordinated behavior, examining the trade-off between weakly and strongly integrated models and their ability to capture broad versus tightly aligned coordination patterns. By contrasting monomodal, flattened, and multimodal methods, we evaluate the distinct contributions of each modality and the impact of different integration strategies. Our findings show that while not all modalities provide unique insights, multimodal analysis consistently offers a more informative representation of coordinated behavior, preserving structures that monomodal and flattened approaches often lose. This work enhances the ability to detect and analyze coordinated online behavior, offering new perspectives for safeguarding the integrity of digital platforms.

翻译：协同在线行为，从有益的集体行动到有害的操纵（如虚假信息宣传活动），已成为数字生态系统分析的一个关键焦点。传统方法通常依赖于单模态方法，专注于单一类型的交互（如共同转发或共同使用话题标签），或者独立地考虑多种模态。然而，这些方法可能忽视了多模态协同中固有的复杂动态。本研究比较了实现多模态协同行为的不同方式，考察了弱集成模型与强集成模型之间的权衡，以及它们捕捉广泛协同模式与紧密对齐协同模式的能力。通过对比单模态、扁平化和多模态方法，我们评估了每种模态的独特贡献以及不同集成策略的影响。我们的研究结果表明，虽然并非所有模态都能提供独特的见解，但多模态分析始终能提供信息更丰富的协同行为表征，保留了单模态和扁平化方法常常丢失的结构。这项工作增强了检测和分析协同在线行为的能力，为维护数字平台的完整性提供了新的视角。