---
title: "Timing In stand-up Comedy — paper by Zribi et al. (2026)"
description: "Research on humor in computing has mostly focused on text, while stand-up comedy also depends on the performer's body… 18 references."
authors: ["Yaelle Zribi", "Florian Cafiero", "Vincent Lépinay", "Chahan Vidal-Gorène"]
year: 2026
venue: "Proceedings of the 2nd Workshop on Computational Humor (CHum 2026)"
source: "https://aclanthology.org/2026.chum-1.2/"
doi: "10.18653/v1/2026.chum-1.2"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zribi-2026-timing-stand-up-comedy-text/"
---
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# Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing

**Authors:** Yaelle Zribi, [Florian Cafiero](https://md.research.elamur.ai/free/en/authors/florian-cafiero/), Vincent Lépinay and [Chahan Vidal-Gorène](https://md.research.elamur.ai/free/en/authors/chahan-vidal-gorene/) · **Year:** 2026 · **Venue:** [Proceedings of the 2nd Workshop on Computational Humor (CHum 2026)](https://md.research.elamur.ai/free/en/venues/chum/) ([Workshop on Computational Humor 2026](https://md.research.elamur.ai/free/en/venues/chum/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.chum-1.2/) · [PDF](https://aclanthology.org/2026.chum-1.2.pdf) · [DOI 10.18653/v1/2026.chum-1.2](https://doi.org/10.18653/v1/2026.chum-1.2) · [OpenAlex](https://openalex.org/W7166821722)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Research on humor in computing has mostly focused on text, while stand-up comedy also depends on the performer's body, camera editing, and audience reaction. TIC-TALK addresses this gap with a multimodal corpus and an open pipeline for measuring comedic timing. It covers 90 Netflix stand-up specials from 2015–2024. Subtitles are split into 60-second thematic segments with BERTopic and sentence embeddings. Whisper-AT detects laughter, a fine-tuned YOLOv8-cls model labels shot types, and YOLOv8s-pose extracts skeletal keypoints. From these keypoints the authors compute continuous movement signals. Each stream keeps its own time resolution and is linked to the others through hierarchical temporal containment rather than resampling.

A descriptive analysis across 24 topics finds that audiences laugh more when the comedian moves less, which fits a pattern of stillness before the punchline. A laughter-onset prediction benchmark shows that past laughter is the strongest cue, and that adding visual features improves results only slightly. The resource lets researchers study the coordination of speech, gesture, editing, and audience response at scale.

## Key points
- **Corpus scale:** more than 5,400 aligned topic segments from 90 specials. Each segment stores a 384-dim all-MiniLM-L6-v2 embedding. Code is on GitHub (yaelle-z/TIC-TALK), and the dataset is planned for Hugging Face (ENC-PSL/TIC-TALK).
- **Time resolutions:** Whisper-AT runs at a 0.8 s stride, and YOLOv8s-pose extracts 17-joint keypoints at 1 fps. The keypoints yield three signals: arm spread, kinetic energy, and trunk lean.
- **Kinetic energy and laughter:** kinetic energy correlates negatively with laughter rate across topics (r = −0.75, N = 24).
- **Topic and shot effects:**
  - Personal and bodily topics draw more laughter than geopolitical ones.
  - The share of close-up shots correlates weakly and positively with laughter (r = +0.28).
  - Belly laughs almost never appear at the topic level.
- **Onset prediction benchmark:** the task is to predict whether laughter starts within the next 2 s, using 285,916 anchors and a show-level split.
  - Laughter history alone reaches AUROC 0.643.
  - Full multimodal fusion reaches AUROC 0.647 and AUPRC 0.277, against a random baseline of 0.170.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Université Paris Sciences et Lettres](https://md.research.elamur.ai/free/en/universities/universite-paris-sciences-et-lettres/) — University, Paris, France · 22 works · 3,290 citations
- [École Nationale des Chartes](https://md.research.elamur.ai/free/en/universities/ecole-nationale-des-chartes/) — University, Paris, France · 4 works · 14 citations
- [École Pour l'Informatique et les Techniques Avancées](https://md.research.elamur.ai/free/en/universities/ecole-pour-l-informatique-et-les-techniques-avancees/) — University, Le Kremlin-Bicêtre, France · 2 works · 20 citations

Also on the byline: [Institut d'Etudes Politiques de Paris](https://ror.org/05fe7ax82) · [médialab Sciences Po](https://ror.org/03vk15693).

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## Cite

Cite as: Yaelle Zribi, Florian Cafiero, Vincent Lépinay and Chahan Vidal-Gorène. 2026. Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing. Proceedings of the 2nd Workshop on Computational Humor (CHum 2026).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{zribi2026timing,
  title = {{Timing In stand-up Comedy: Text, Audio, Laughter, Kinesics (TIC-TALK): Pipeline and Database for the Multimodal Study of Comedic Timing}},
  author = {Yaelle Zribi, Florian Cafiero, Vincent Lépinay and Chahan Vidal-Gorène},
  booktitle = {Proceedings of the 2nd Workshop on Computational Humor (CHum 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.chum-1.2},
  url = {https://doi.org/10.18653/v1/2026.chum-1.2}
}
```

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## References

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*Updated 2 November 2026*