发表机构
School of Computer Science and Mathematics; Faculty of Engineering, Computing and the Environment; Kingston University London（计算机科学与数学学院; 工程、计算与环境学院; 伦敦金斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对有影响力公共话语的误导性，提出以读者为中心的RCMN框架构建数据集，发现轻量表征可恢复读者解读但难识别误导机制，为可扩展误导性分析提供了新方向。
AI 中文摘要
有影响力的公共话语会塑造公众信念，也可能产生误导，其方式不仅包括明确陈述的内容，还包括信息的框架构建、省略、情境化及传播方式。然而，针对此类误导性如何产生以及如何影响读者解读的研究较少。为填补这一空白，我们提出以读者为中心的误导性理解框架（Reader-Centric Misleadingness Understanding, RCMN），该框架从五个维度对误导性进行可操作化定义：误导机制、读者可能形成的解读、证据支持的解读、情绪唤起程度及传播意图。基于此框架，我们构建了一个有证据支撑的有影响力公共话语数据集。实证研究结果表明，误导性具有多样性，远不止于虚构，无依据的推断、夸张和省略是常见的误导机制，且常与更高的情绪唤起程度及扭曲的传播意图相关联。此外，我们研究了仅使用轻量的主张与情境表征，在无法获取更丰富的情境、证据及多模态信息时，是否仍保留足够线索以理解以读者为中心的误导性。对五个近期生成式基础模型的评估显示，通过这类有限表征通常可恢复读者层面的解读，而识别误导性的产生机制则更具挑战性。这些发现凸显了轻量表征在可扩展误导性分析中的潜力，而可靠理解误导机制仍需要更丰富的情境和证据支撑。
英文摘要
Influential public discourse shapes public beliefs and can also mislead, not only through what is stated, but also through how information is framed, omitted, contextualised, and communicated. Yet less research has focused on how such misleadingness arises and shapes the interpretations formed by readers. To address this gap, we introduce Reader-Centric Misleadingness Understanding (RCMN), a framework that operationalises misleadingness through five dimensions: misleading mechanism, likely reader interpretation, evidence-warranted interpretation, emotional arousal, and communicative intent. Based on this framework, we construct an evidence-grounded dataset of influential public discourse. Empirical findings show that misleadingness is diverse and extends well beyond fabrication, with unsupported inference, exaggeration, and omission among the prevalent mechanisms, and is frequently associated with heightened emotional arousal and distortive communicative intent. Moreover, we investigate whether lightweight claim-and-context representations retain sufficient cues for understanding reader-centric misleadingness without access to richer contextual, evidential, and multimodal information. Evaluation across five recent generative foundation models shows that reader-level interpretations can often be recovered from such limited representations, whereas identifying how misleadingness is produced remains considerably more challenging. These findings highlight the potential of lightweight representations for scalable misleadingness analysis, while reliable understanding of misleading mechanisms continues to require richer contextual and evidential grounding.
