Mapping Big Data and Artificial Intelligence in arts and humanities across time: an exploratory scientometric analysis based on the WoS database. Issue 1 (April 2020)

• Record Type: Journal Article
• Authors: Xu, Zhichao; Feng, Yuan; Liao, Han-Teng; Jiang, Xiaolin
• Is Part Of: IOP conference series. Volume 806:Issue 1(2020)
• Publication Date: 2020-04
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/806/1/012011
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The ways in which artificial intelligence (AI) and Big Data shape the work and the role of artists and humanities scholars have been at the forefront discussions among advanced universities and institutions, including examples such as computer-generated artwork, ethics, and philosophy, etc. With the aim to systematically review the related literature in arts and humanities, it is important to examine the discipline dynamics across time, thereby exploring both historical and current status. Based on the bibliographic data of 2, 707 articles collected from Web of Science from 1956 to 2020, this paper has identified main disciplines and publications across three time periods, using various clustering and visualization methods. Based on the analytical and visualization findings, this article provides both a roadmap and data processes for artists, researchers, and policy-makers to understand how AI and Big Data have been discussed in arts and humanities disciplines.