Disinformation has become a central challenge in the digital information ecosystem, yet the defining property that distinguishes it from misinformation-intent-remains poorly represented in computational detection and moderation research. This paper synthesizes how intent is conceptualized and operationalized across the technical and policy literatures. Through a systematic review of 84 algorithmic papers on disinformation and information/influence operations (IO) detection, we assess how intent is treated in definitions, modeling, data provenance, and labeling protocols. Our analysis reveals four major gaps: (1) conceptual inconsistency; (2) operationalization deficiency; (3) institutional misalignment; and (4) governance frameworks. We propose a roadmap toward intent-aware disinformation detection and moderation that bridges definitional rigor with methodological practice, advancing both theoretical coherence and policy relevance in combating disinformation.

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