Grounding Information Disorder in NLP: A Theoretical and Operational Framework

Abstract

This position paper proposes a theory grounded NLP framework for information disorder detection integrating three explicitly connected dimensions: epistemic status, intentionality, and contextual harm. Moving beyond binary fake news classification, we argue that reliable intervention requires structured differentiation between verification outcomes, manipulation indicators, and consequence assessment. We provide concrete annotation schemas with decision rules for ambiguous cases, formal aggregation operators with monotonicity and escalation guarantees, explicit conflict resolution strategies for inconsistent signals, and standardized risk profile templates that translate multidimensional outputs into actionable routing policies. Synthesizing work on harm taxonomies, uncertainty quantification, and automated fact checking pipelines, we introduce an integration layer that preserves interpretability while enabling policy aligned deployment. We further propose a reformed evaluation protocol incorporating conformal prediction for principled abstention, calibration analysis, disagreement modeling, harm weighted metrics, and human uplift assessment to measure real decision support utility rather than standalone classifier accuracy. We position this framework as a conceptual and operational roadmap for structured misinformation assessment, outlining phased validation pathways while acknowledging that empirical validation remains essential future work.

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