Explainable AI in Content Moderation: Global, Local, and Narrative Approaches to Trust and Plausibility

We have explored various explainable AI scopes in fake news detection and found individuals trust AI when its explanation feels plausible. Drawing on the Heuristic–Systematic Model, we claim that plausibility mainly drives reliance. In an experiment, we compared seven increasingly detailed explanation formats—ranging from a bare flag to keyword lists, token-highlight views, and two levels of narrative—while controlling model accuracy. Trust did not rise monotonically with extra detail. Unedited token highlights lowered trust relative to the keyword list; pruning irrelevant tokens restored it; only the contextual narrative produced the highest trust. Mediation analysis confirmed the theory: the plausibility → trust path was β ≈ 0.55 (p < .001), and all indirect effects were significant. Our findings suggest that, in content-moderation settings, explanations that foreground plausibility through concise, context-anchored narratives can foster higher reviewer trust than simply exposing additional model internals.