SOURCE-LINKED INTELLIGENCE
RCMN: Understanding Misleadingness in Influential Public Discourse
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. Ba
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T16:57:40.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.