The Development of the Journal Environment of Leonardo

We present animations based on the aggregated journal-journal citations of Leonardo during the period 1974-2008. Leonardo is mainly cited by journals outside the arts domain for cultural reasons, for example, in neuropsychology and physics. Articles in Leonardo itself cite a large number of journals, but with a focus on the arts. Animations at this level of aggregation enable us to show the history of the journal from a network perspective

Maps on the basis of the Arts & Humanities Citation Index: The journals Leonardo and Art Journal versus "Digital Humanities" as a topic

The possibilities of using the Arts & Humanities Citation Index (A&HCI) for journal mapping have not been sufficiently recognized because of the absence of a Journal Citations Report (JCR) for this database. A quasi-JCR for the A&HCI (2008) was constructed from the data contained in the Web-of-Science and is used for the evaluation of two journals as examples: Leonardo and Art Journal. The maps on the basis of the aggregated journal-journal citations within this domain can be compared with maps including references to journals in the Science Citation Index and Social Science Citation Index. Art journals are cited by (social) science journals more than by other art journals, but these journals draw upon one another in terms of their own references. This cultural impact in terms of being cited is not found when documents with a topic such as "digital humanities" are analyzed. This community of practice functions more as an intellectual organizer than a journal

The structure of the Arts & Humanities Citation Index: A mapping on the basis of aggregated citations among 1,157 journals

Using the Arts & Humanities Citation Index (A&HCI) 2008, we apply mapping techniques previously developed for mapping journal structures in the Science and Social Science Citation Indices. Citation relations among the 110,718 records were aggregated at the level of 1,157 journals specific to the A&HCI, and the journal structures are questioned on whether a cognitive structure can be reconstructed and visualized. Both cosine-normalization (bottom up) and factor analysis (top down) suggest a division into approximately twelve subsets. The relations among these subsets are explored using various visualization techniques. However, we were not able to retrieve this structure using the ISI Subject Categories, including the 25 categories which are specific to the A&HCI. We discuss options for validation such as against the categories of the Humanities Indicators of the American Academy of Arts and Sciences, the panel structure of the European Reference Index for the Humanities (ERIH), and compare our results with the curriculum organization of the Humanities Section of the College of Letters and Sciences of UCLA as an example of institutional organization

Evolution of Wikipedia's Category Structure

Wikipedia, as a social phenomenon of collaborative knowledge creating, has been studied extensively from various points of views. The category system of Wikipedia, introduced in 2004, has attracted relatively little attention. In this study, we focus on the documentation of knowledge, and the transformation of this documentation with time. We take Wikipedia as a proxy for knowledge in general and its category system as an aspect of the structure of this knowledge. We investigate the evolution of the category structure of the English Wikipedia from its birth in 2004 to 2008. We treat the category system as if it is a hierarchical Knowledge Organization System, capturing the changes in the distributions of the top categories. We investigate how the clustering of articles, defined by the category system, matches the direct link network between the articles and show how it changes over time. We find the Wikipedia category network mostly stable, but with occasional reorganization. We show that the clustering matches the link structure quite well, except short periods preceding the reorganizations.Comment: Preprint of an article submitted for consideration in Advances in Complex Systems (2012) http://www.worldscinet.com/acs/, 19 pages, 7 figure

A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences

The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of articles indexed between 2015 and 2020 in Social Sciences Citation Index (SSCI) of the Web of Science repository. We have analysed especially the recent research direction called Computational Social Sciences (CSS) that bridges computer analytical approaches with social science challenges, generating new methodologies of Big Data and AI analytics for social sciences. The results indicate that AI and Big Data practices are not confined to CSS only and are diffused in a wide variety of disciplines under Social Sciences and are made use of in many main research lines as well. Thus, the anticipated overlap between the Social Sciences & AI specialization and CSS has yet to be crystallised. Moreover, the impact of computational social science studies is not permeated to social science citation networks yet. Lastly, we demonstrate that the AI and Big Data publications that appear under the SSCI index are more oriented towards computational studies than addressing social science concepts, concerns, and challenges

A Bibliometric Analysis of the Use of Artificial Intelligence Technologies for Social Sciences

The use of Artificial Intelligence (AI) and Big Data analysis algorithms is complementary to theory-driven analysis approaches and becoming more popular also in social sciences. This paper describes the use of Big Data and computational approaches in social sciences by bibliometric analyses of articles indexed between 2015 and 2020 in Social Sciences Citation Index (SSCI) of the Web of Science repository. We have analysed especially the recent research direction called Computational Social Sciences (CSS) that bridges computer analytical approaches with social science challenges, generating new methodologies of Big Data and AI analytics for social sciences. The results indicate that AI and Big Data practices are not confined to CSS only and are diffused in a wide variety of disciplines under Social Sciences and are made use of in many main research lines as well. Thus, the anticipated overlap between the Social Sciences & AI specialization and CSS has yet to be crystallised. Moreover, the impact of computational social science studies is not permeated to social science citation networks yet. Lastly, we demonstrate that the AI and Big Data publications that appear under the SSCI index are more oriented towards computational studies than addressing social science concepts, concerns, and challenges

Toward inclusivity: Virtual reality museums for the visually impaired

The access to virtual reality museums mostly relies on the visual sense, making it difficult if not impossible for visually impaired people to partake in the experience. We present a between-subjects study exploring if narrations and spatialized 'reference' audio combined with haptic feedback can be a sufficient replacement for the traditional use of vision in a virtual reality art museum. Our pilot study compares two implementations: A standard 'sighted' version that provides visual artifacts along with related acoustic narratives, a 'visually impaired' version with modified narratives and enhanced audio and haptics that was tested by visually impaired participants, as well as 'blindfolded' sighted individuals. Auditory and haptic feedback in the latter version were used to steer visitors towards specific virtual objects. Although the experiences of the visually impaired were obviously not statistically equivalent to the non-impaired group, results show that our method enabled them to experience the virtual reality museum adequately and find objects faster due to the additional auditory and haptic feedback

Hidden in a Breath: Tracing the Breathing Patterns of Survivors of Traumatic Events

Abstract of paper 0982 presented at the Digital Humanities Conference 2019 (DH2019), Utrecht , the Netherlands 9-12 July, 2019