Stories That Big Danmaku Data Can Tell as a New Media

Abstract

In online video communities, an emerging source of comment, such as danmaku, allows viewers to interact when watching. Prior work discussed the feasibility of application using danmaku, while the comprehensive analysis of large-scale data is vacant to be filled in. We here release our danmaku data collection and report interesting observed phenomena in the danmaku comments. This analysis reveals the temporal distributions and user's access patterns of online time-sync comments. In particular, we distribute two novel natural language processing (NLP) tasks based on our danmaku dataset and provide baseline models. In the first task, we show how the naive models predict positive or negative sentiment given a danmaku comment, which effectively extends the real applications such as opinion poll prediction and marketing investigation. In the second task, we propose to use the NLP summarization model to make video tagging and summarization. The experimental results suggest that danmaku can not only support deeper and richer interactions between viewers and videos but also with high research value.