@inproceedings{zribi-etal-2026-timing,
    title = "Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics ({TIC}-{TALK}): Pipeline and Database for the Multimodal Study of Comedic Timing",
    author = "Zribi, Yaelle  and
      Cafiero, Florian  and
      L{\'e}pinay, Vincent  and
      Vidal-Gor{\`e}ne, Chahan",
    editor = "Amir, Ori  and
      Hempelmann, Christian F.  and
      Rayz, Julia  and
      Dong, Tiansi  and
      Miller, Tristan",
    booktitle = "Proceedings of the 2nd Workshop on Computational Humor ({CH}um 2026)",
    month = jul,
    year = "2026",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.chum-1.2/",
    pages = "29--38",
    ISBN = "979-8-89176-431-6",
    abstract = "Stand-up comedy, and humor in general, are often studied through their verbal content. Yet live performance relies just as much on embodied presence and audience feedback. We introduce TIC-TALK, a multimodal resource with 5,400+ temporally aligned topic segments capturing language, gesture, and audience response across 90 professionally filmed stand-up comedy specials (2015{--}2024). The pipeline combines BERTopic for 60 s thematic segmentation with dense sentence embeddings, Whisper-AT for 0.8 s laughter detection, a fine-tuned YOLOv8-cls shot classifier, and YOLOv8s-pose for raw keypoint extraction at 1 fps. Raw 17-joint skeletal coordinates are retained without prior clustering, enabling the computation of continuous kinematic signals{---}arm spread, kinetic energy, and trunk lean{---}that serve as proxies for performance dynamics. All streams are aligned by hierarchical temporal containment without resampling, and each topic segment stores its sentence-BERT embedding for downstream similarity and clustering tasks. As a concrete use case, we study laughter dynamics across 24 thematic topics: kinetic energy negatively predicts audience laughter rate (r={\ensuremath{-}}0.75,N= 24), consistent with a stillness-before-punchline pattern; personal and bodily content elicits more laughter than geopolitical themes; and shot close-up proportion correlates positively with laughter (r= +0.28), consistent with reactive montage."
}