  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.
