Visual Analytics of Political Networks From Face-Tracking of News Video

The rich nature of news makes it a classic subject of visual analytics research. Such analysis is often based on rich textual data. However, we want to test how much we can understand the news from video information through face detection and tracking. Towards this goal, we propose a visual analytics system and discuss its design and implementation to support media experts in understanding political interactions in an archive of 12 years of the Japanese public broadcaster NHK's News 7 program. After identifying the tasks and abstraction required for our analysis, we construct links from face detection and tracking to derive multiple political networks. Our proposed design embeds this rich data into a visual analytics framework that presents four levels of abstraction: time period, network, timeline, and face-tracks within video. We present how the exploration of the archive with our system results in good understanding of the Japanese politico-media scene during these 12 years while finding evidence of “presidentialization” of the media.