# DeHumor: Visual Analytics for Decomposing Humor

arXiv: https://arxiv.org/abs/2107.08356 · HTML: https://mlsanity.com/paper/2107.08356/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 73 references that are in the map.
- First posted on arXiv 18 Jul 2021.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [UR-FUNNY: A Multimodal Language Dataset for Understanding Humor](https://mlsanity.com/paper/1904.06618/) (2019)
- [Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest](https://mlsanity.com/paper/1506.08126/) (2015)
- [EmoCo: Visual Analysis of Emotion Coherence in Presentation Videos](https://mlsanity.com/paper/1907.12918/) (2019)
- [VoiceCoach: Interactive Evidence-based Training for Voice Modulation Skills in Public Speaking](https://mlsanity.com/paper/2001.07876/) (2020)

## Built on by (most cited first, 5 of 5)

- [ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models](https://mlsanity.com/paper/2306.04563/) (2023)
- [XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis](https://mlsanity.com/paper/2301.10385/) (2023)
- [A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future](https://mlsanity.com/paper/2412.14056/) (2024)
- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [GestureLens: Visual Analysis of Gestures in Presentation Videos](https://mlsanity.com/paper/2204.08894/) (2022)

## Abstract (arXiv)

Despite being a critical communication skill, grasping humor is challenging -- a successful use of humor requires a mixture of both engaging content build-up and an appropriate vocal delivery (e.g., pause). Prior studies on computational humor emphasize the textual and audio features immediately next to the punchline, yet overlooking longer-term context setup. Moreover, the theories are usually too abstract for understanding each concrete humor snippet. To fill in the gap, we develop DeHumor, a visual analytical system for analyzing humorous behaviors in public speaking. To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features and provides inline annotations of them on the video script. In particular, to better capture the build-ups, we introduce content repetition as a complement to features introduced in theories of computational humor and visualize them in a context linking graph. To help users locate the punchlines that have the desired features to learn, we summarize the content (with keywords) and humor feature statistics on an augmented time matrix. With case studies on stand-up comedy shows and TED talks, we show that DeHumor is able to highlight various building blocks of humor examples. In addition, expert interviews with communication coaches and humor researchers demonstrate the effectiveness of DeHumor for multimodal humor analysis of speech content and vocal delivery.