Abstract
Among the many existing approaches to analyzing culture, the trend toward data-driven methodologies has given rise to a wide range of novel perspectives on cultural phenomena. Alongside quantitative textual analysis, recent scholarship emphasizes the potential of web native data for understanding social processes, or engages with new types of data extracted computationally from digitized cultural artifacts. In this paper, we explore the new direction of metadata analytics distinct from bibliometrics and scientometrics, which takes advantage of a wide range of descriptive metadata resources compiled by enthusiast groups online in order to understand cultural processes and phenomena at scale. In our concrete case, this involves large quantities of metadata on Japanese visual media, which allow us to develop, among other things, a contextual understanding of developments in anime, manga and video game culture. In following this pathway, however, we also need to take the limitations descriptive metadata impose on us seriously. The nascent field of critical data studies has already made strides towards addressing the potential threats and challenges that arise in working with big data. Descriptive metadata foreground a specific set of challenges: they appear neutral or factual, and as such are even more likely to be taken at face value. Descriptive metadata, however, are just as much constructing their objects as they are describing them. Metadata Analytics needs to take these peculiarities into account in its approach.
Presenters
Martin RothResearch Fellow / Associate Professor, Stuttgart Media University / Ritsumeikan University, Japan Zoltan Kacsuk
Postdoctoral Researcher for the Japanese Visual Media Graph Project, Institute for Applied Artificial Intelligence, Stuttgart Media University, Baden-Württemberg, Germany
Details
Presentation Type
Paper Presentation in a Themed Session
Theme
2021 Special Focus—The Data Galaxy: The Un-Making of Typographic Man?
KEYWORDS
Metadata Analytics, Japanese Visual Media, Critical Data Studies
