Confusion, Humor, and Attribution: A Micro-Dataset of Audience Sensemaking Responses to Non-Instructional AI Media

This dataset documents a short sequence of public audience responses to experimental AI-adjacent media on YouTube, captured as three timestamped screenshots of comment threads. The comments exhibit recurring sensemaking strategies—expressions of confusion, humor, attribution to altered mental states, and conversational collapse—when viewers encounter content that resists clear genre classification or explanatory framing. Rather than treating such responses as noise, the dataset preserves them as ethnographic artifacts illustrating how platform users manage interpretive uncertainty in public settings. The author’s minimal, non-corrective replies are included as part of the interactional context. The dataset is intended for researchers studying digital ethnography, AI discourse, online sensemaking, and the social dynamics of ambiguity in algorithmic media environments.