What makes readers laugh?: value of sensing laughter for humor webtoon

Abstract

Webtoon is a popular content in South Korea that has more fun techniques by using both IT and cartoon elements. However, the rating system for webtoon is still unsatisfying which have limitations on comprehending users' unconscious behavior. In this paper, we explore the value of using users' laughter reaction data for humor webtoons. Users' laughter reaction data and the rating scores were extracted simultaneously in user observation. As a result, the laughter reaction significantly correlates with the manual rating score. Also, we elicited each participants' flow of laughter which enabled to understand their laughter behavior and scenes that were attractive. With those data, ideation was conducted to generate ideas on how laughter reaction data can be used in new ways for humor webtoons. Thus, we proposed the potential values that suggest viable solutions of capturing laughter reactions for humor webtoons.