arXiv:2507.12108cs.SIcs.AI2025-07被引 3

多模态协同行为分析揭示了整合策略的权衡与优势

Multimodal Coordinated Online Behavior: Trade-offs and Strategies

  • 对比单模态、扁平化与多模态融合方法,评估不同整合策略
  • 多模态分析能更好保留紧密协同结构,优于单一或简单合并方式
  • 适合研究网络操纵、信息传播等数字生态安全问题的学者

协同在线行为从有益集体行动到有害操纵(如虚假信息传播)已成为数字生态系统分析的关键。传统方法多采用单模态分析,仅关注如共同转发或共用标签等单一互动类型,或独立处理多模态数据。然而,这些方法可能忽略多模态协同中的复杂动态。本研究比较了多模态协同行为的不同操作化方式,探讨弱整合与强整合模型之间的权衡,及其捕捉广泛与紧密对齐协调模式的能力。通过对比单模态、扁平化和多模态方法,我们评估了各模态的独立贡献及不同整合策略的影响。结果表明,尽管并非所有模态都提供独特见解,但多模态分析始终能更全面地表征协同行为,保留单模态与扁平化方法常丢失的结构。该研究提升了检测与分析协同在线行为的能力,为保障数字平台完整性提供了新视角。

原文摘要 · Abstract (English)

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.

协同行为多模态分析数字安全

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