Theoretical article analyzes concealed grooming strategies in short-form video advice, emphasizing prevention.
Public relationship and dating advice on short-form video platforms increasingly appears in aestheticized, entertainment-forward forms that do not resemble the private text exchanges that dominate much online grooming detection research. This theoretical article introduces the Aesthetic-to-Risk Framework of Masked Manipulation (ART-MM), a conceptual framework for interpreting how grooming-relevant relational and boundary cues can be packaged as ordinary advice in public short-form video. ART-MM defines masked manipulation as multimodal public communication in which relational cues associated with early-stage sexual grooming are embedded within familiar advice, dating, lifestyle, or self-help genres, so that manipulation-consistent scripts appear benign, humorous, affirming, or normative. The framework has two linked components: a Manifest Aesthetic Mask, consisting of observable visual, audio, editing, and on-screen text features, and Covert Relational and Boundary Cues, consisting of relational scripts such as rapid trust-building, flattery, boundary testing, secrecy normalization, isolation, desensitization, coercive care framing, manipulative validation, age-gap normalization, and exclusivity. Grounded in Bandura’s social cognitive theory, ART-MM explains how audiovisual packaging may shape attention, retention, and motivation without requiring claims about creator intent or viewer harm. The article distinguishes masked manipulation from ordinary relationship advice, evidence-based prevention content, explicit solicitation, and generic influencer self-help; proposes a three-part typology of normative relational framing, aestheticized boundary erosion, and escalatory grooming-consistent signaling; and establishes ethical guardrails against creator profiling, diagnostic labeling, and surveillance-oriented use. ART-MM is offered as a prevention-oriented vocabulary for future qualitative, multimodal, and mixed-methods research.
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H.N. Landress (2026) studied this question.
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