36 research outputs found
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
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
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
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
Need to categorize: A comparative look at the categories of the Universal Decimal Classification system (UDC) and Wikipedia
This study analyzes the differences between the category structure of the
Universal Decimal Classification (UDC) system (which is one of the widely used
library classification systems in Europe) and Wikipedia. In particular, we
compare the emerging structure of category-links to the structure of classes in
the UDC. With this comparison we would like to scrutinize the question of how
do knowledge maps of the same domain differ when they are created socially
(i.e. Wikipedia) as opposed to when they are created formally (UDC) using
classificatio theory. As a case study, we focus on the category of "Arts".Comment: Paper for High Throughput Humanities - a satellite meeting at the
European Conference on Complex Systems 2010; Sept. 15, 2010 Lisbon University
Institute ISCTE, Lisbon, Portuga
Towards a quieter Neonatal Intensive Care Unit: Current approaches and design opportunities
Recent studies show that the well-being of patients and the performance of healthcare professionals in modern neonatal intensive care units (NICUs) are severely affected by the amount of auditory alarms and sound nuisance. This paper presents a semi-systematic review on the topic of environmental sound in the NICU, where current themes, insights, and limitations are highlighted. Furthermore, it outlines the results of an observation of the NICU environment and an interview with nurses at Erasmus Medical Center, in order to understand the users, their context, and the technology that can enable design interventions. The insights gathered from the literature and the users, together with a technology search, lead to potential design opportunities to be developed further. Based on these, we propose a technological solution towards a healthy sound environment in the NICU
Need to Categorize: A Comparative Look at the Categories of Universal Decimal Classification System and Wikipedia
A Computational Look at Oral History Archives
Computational technologies have revolutionized the archival sciences field, prompting new approaches to process the extensive data in these collections. Automatic speech recognition and natural language processing create unique possibilities for analysis of oral history (OH) interviews, where otherwise the transcription and analysis of the full recording would be too time consuming. However, many oral historians note the loss of aural information when converting the speech into text, pointing out the relevance of subjective cues for a full understanding of the interviewee narrative. In this article, we explore various computational technologies for social signal processing and their potential application space in OH archives, as well as neighboring domains where qualitative studies is a frequently used method. We also highlight the latest developments in key technologies for multimedia archiving practices such as natural language processing and automatic speech recognition. We discuss the analysis of both visual (body language and facial expressions), and non-visual cues (paralinguistics, breathing, and heart rate), stating the specific challenges introduced by the characteristics of OH collections. We argue that applying social signal processing to OH archives will have a wider influence than solely OH practices, bringing benefits for various fields from humanities to computer sciences, as well as to archival sciences. Looking at human emotions and somatic reactions on extensive interview collections would give scholars from multiple fields the opportunity to focus on feelings, mood, culture, and subjective experiences expressed in these interviews on a larger scale
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