Misleadingness in public discourse is diverse and often subtle (exaggeration, omission, unsupported inference), and while AI can sometimes predict how readers will interpret misleading content, identifying *how* it misleads requires rich contextual and evidential grounding.
This paper introduces RCMN, a framework for understanding how public discourse misleads readers through framing, omission, and context rather than just false claims.