DeHumor: Visual Analytics for Decomposing Humor
Xingbo Wang, Yao Ming, Tongshuang Wu, Haipeng Zeng, Yong Wang, Huamin, Qu

TL;DR
DeHumor is a visual analytical system that decomposes humorous videos into multimodal features, revealing humor building blocks and aiding understanding of humor in public speaking through visualizations and annotations.
Contribution
It introduces a novel visual analytics approach for analyzing humor by decomposing videos into multimodal features and visualizing humor build-ups, addressing limitations of prior textual and audio feature focus.
Findings
DeHumor successfully highlights humor building blocks in videos.
Expert feedback confirms DeHumor's effectiveness for humor analysis.
Case studies demonstrate the system's ability to analyze diverse humor examples.
Abstract
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…
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