<p>As social media increasingly function as platforms for information communicating, they have become key venues for health information seeking while also enabling the rapid diffusion of misinformation. This study examines how linguistic characteristics of misinformation and information consistency between original posts and User-Generated Content (UGC) shape misinformation diffusion, with influencer status considered as a moderating factor. Using a hybrid analytical approach that combines text mining and negative binomial regression, we analyze 10,651 misinformation posts and 615,562 related UGC items concerning winter influenza and vaccines on <i>Rednote</i>. Consequently, we reveal that syntactic linguistic features, including sentence length, word composition, and emoji usage, are significantly associated with all three diffusion-related engagement behaviors, i.e., likes, comments, and reposts. Emotional cues exhibit heterogeneous effects across engagement types, where anxiety and anger suppress diffusion-related behaviors while sadness significantly increases reposting. Interestingly, users are more likely to comment on misinformation when UGC is topically similar to the original post, rather than simply liking or reposting it, suggesting evaluative engagement rather than passive endorsement. Further, at the pragmatic level, metaphorical expression and information consistency across syntactic, semantic, and pragmatic dimensions between source content and UGC significantly amplify misinformation diffusion. Importantly, influencer status positively moderates these relationships, strengthening the effects of linguistic features and information consistency, particularly for reposting. Overall, this study advances misinformation diffusion research by integrating linguistic, cross-text, and social influence mechanisms, and provides actionable insights for platform governance aimed at balancing user engagement with information integrity in digital information platforms.</p>