A Study on Anxiety Reduction of Reader-dependent "Jirai" Expressions in Comics.

Since comics are diverse, there are some depictions that readers like and some that they do not like. If readers find a part of a comic that they dislike, they can continue reading it even if they can skip that part, but there is a possibility that they will stop reading it entirely if they actually read it. Therefore, we propose a method for allowing readers to enjoy comics without worrying about depictions that they do not like. To realize this method, we developed a system that allows readers to flag depictions they dislike while reading comics and conducted data collection experiments. We also implemented a system in which jirai (content to be avoided) flags and announcements are given to readers while reading a comic, and we examined the feasibility of the system by operating it for about four weeks. We confirmed that the flagging and jirai announcements were performed during the system operation. In addition, the evaluation of jirai judgments for comics using the Vision API showed that while there is potential for AI to make judgments, there are still difficulties in judging detailed depictions. [ABSTRACT FROM AUTHOR]