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DeHumor: Visual Analytics for Decomposing Humor.

Xingbo Wang, Yao Ming, Tongshuang Wu

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    Understanding humor in public speaking is complex. DeHumor, a new visual system, analyzes multimodal features like content repetition and vocal delivery to reveal humor

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    Area of Science:

    • Computational linguistics
    • Human-computer interaction
    • Communication studies

    Background:

    • Humor is a critical communication skill, yet challenging to grasp.
    • Existing computational humor research often overlooks long-term context and provides abstract theories.
    • A need exists for intuitive analysis of concrete humor examples in public speaking.

    Purpose of the Study:

    • To develop DeHumor, a visual analytical system for dissecting humorous behaviors in public speaking.
    • To intuitively reveal the building blocks of humor by decomposing videos into multimodal features.
    • To complement existing theories with content repetition for better capture of humor build-ups.

    Main Methods:

    • Decomposition of humorous videos into multimodal features (textual, audio, visual).
    • Inline annotation of features on video scripts.
    • Introduction and visualization of content repetition using a context linking graph.
    • Summarization of content keywords and humor feature statistics on an augmented time matrix.

    Main Results:

    • DeHumor successfully highlights various building blocks of humor in case studies (stand-up comedy, TED talks).
    • The system effectively visualizes multimodal features and their contribution to humor.
    • Content repetition is identified as a valuable feature for analyzing humor build-up.

    Conclusions:

    • DeHumor provides an effective tool for multimodal humor analysis in speech content and vocal delivery.
    • The system aids in understanding concrete humor examples by linking abstract theories to specific instances.
    • Expert feedback confirms DeHumor's utility for communication coaches and humor researchers.