Label, Slop, and Signal: A Micro–Digital Ethnography of "AI" Accusation as an Epistemic Shortcut in YouTube Comment Sections

This record contains a qualitative digital ethnography examining a one-month corpus of publicly visible YouTube comments responding to research-oriented audiovisual content involving explicit human–AI collaboration. The dataset documents a recurring pattern in which commenters deploy low-effort labels such as “AI slop,” “this is AI,” or “NotebookLM garbage” as rapid epistemic shortcuts under conditions of evaluative uncertainty. Rather than functioning as substantive critique, these labels operate as interactional signals that enable disengagement, reinforce in-group alignment, and restore perceived epistemic control. Alongside dismissive responses, the corpus also includes technically engaged critiques, reflexive meta-commentary, affiliative reactions, and explicit recognition of comment-section performativity. Together, these artifacts illustrate how collapsing authorship cues in hybrid media environments alter evaluative behavior at scale. The deposit includes: A primary ethnographic analysis paper (PDF) A Methods & Reproducibility appendix A formal comment-taxonomy table The underlying comment corpus used for analysis No private data were collected. All comments were publicly visible at time of collection. No intervention, prompting, or manipulation was performed.