Updated: Aug 3, 2018

The Internet is changing the way arts are produced and consumed. DeviantArt, launched in 2000, has become one of the largest art platform on earth. Based on the idea of sharing and displaying amateur artworks, this social website has turned into a digital platform that has about 40 million monthly visitors. Not a traditional curator or biennial jury, but a network of common people openly dissects, criticizes and improves this giant open museum. Arts in the twenty first century is digital, and this project is about understanding its new dynamics, and developing the set of tools to cope with them.


The category structure of deviantArt

To study a social network site such as deviantArt, we used complex network analysis as our main tool, and did an analysis of dA's network first. With the support of my Veni Grant “DeviantArt: Mapping the Alternative Art World”, I received a data set directly from DeviantArt, of about 100.000 members. Beside the standard complex network approach of analysing links between members, we focused on the category structure of the DA member network, which revealed an unexpected picture. We found out that aside from the category of Photography, most categories do not create clusters of their sub-categories, but rather form mixed clusters according to production techniques.


We furthermore made use of the idea of citation networks, and analyzed the impact of deviantArt specific cite metrics and how these change user behavior. In another study, we tapped into how information flows in deviantArt, and if we can use this as a methodology to follow the impact and spread of artistic styles & genres. A last study that focused on the network of dA was to suggest a new methodology that would combine the use of complex network tools with image analysis tools to get a better understanding of the dA in its entirety.


PUBLICATIONS:


2013. Combining Cultural Analytics and Networks Analysis: Studying a Social Network Site with User-Generated Content. A.A. Akdag Salah, L. Manovich, A.A. Salah and J. Chow. JOBEM, 57(3), 409-426.

Published version & abstract

The high resolution images can be downloaded here: 1, 2, 3.


2013. Flow of Innovation in deviantArt: Following Artists on an Online Social Network Site. A.A. Akdag Salah, A.A. Salah. Mind and Society , 12(1), 137-149.

Published version & abstract


2012. DeviantArt in Spotlight: A Network of Artists. A.A. Akdag Salah, A.A. Salah, B. Buter, N. Dijkshoorn, D. Modolo, Q. Nguyen, S. van Noort, B. van de Poel. Leonardo, 45(5).

Published version & abstract / Author PDF


2011. Explorative visualization and analysis of a social network for arts: The case. Buter, N. Dijkshoorn, D. Modolo, Q. Nguyen, S. van Noort, B. van de Poel, A.A. Akdag Salah, A.A. Salah. Journal of Convergence, 2(2), .87-94.

Published version & abstract / Author PDF

Updated: Aug 3, 2018

As a Digital Humanities scholar, I was curious to understand the dynamics of collaboration and publication behavior inside the discipline. Hence, we analyzed the citation structure of Digital Humanities (DH), and questioned whether a study of DH as a virtual community would help to understand its dynamics better.

Scientometric exploration of Virtual Communities, with the application of overlay maps.


As an interdisciplinary academician collaborating with many scholars from diverse fields, the benefits and drawbacks of collaboration heavily affected my research. I studied the results of big scientific collaborations, especially through two COST Actions of which I was a member of, COST Action MP0801: Physics of Competition and Conflicts and COST ACTION KnowEscape: Analyzing the dynamics of information and knowledge landscapes respectively. We have also looked at the EINS Network of Excellence on Internet Science as a collaboration network, and visualized its collaboration structure.



PUBLICATIONS:


2015. Analysing an Academic Field through the Lenses of Internet Science: Digital Humanities as a Virtual Community. A.A. Akdag Salah, A. Scharnhorst, and S. Wyatt. in Internet Science, Lecture Notes in Computer Science, Volume 9089, pp. 78-89. Springer.

AUTHOR PDF


2013. Mapping EINS: An exercise in mapping the Network of Excellence in Internet Science. A.A. Akdag Salah, A. Scharnhorst, S. Wyatt, S. Passi. 1st International Internet Science Conference, April 2013, Brussels.

AUTHOR PDF


Updated: Aug 3, 2018

Knowledge organization systems are evolving complex systems. While analyzing the representation systems of knowledge, the Knowledge Space Lab project contributed to the new area of “maps of science”.

A Comparison of Wikipedia and UDC categories, and their distribution.

The project developed an innovative research line addressing the difference between representing scholarly knowledge in “external” classifications systems (such as thesauri, ontologies, bibliographic systems) and “internal” representations based on data and user-tagging (such as network analysis, user annotations/tagging, folksonomies).


The aim of the project was to explore how different visual representations can contribute to a better understanding of knowledge dynamics. The project addressed the relationship between dynamic processes in knowledge systems and the dynamics of a reference system in which these processes are analyzed.

Knowledge Space Lab used Wikipedia and UDC datasets to compare two distinctly different classification systems, crowd-sourced, user annotated classification versus expert based, and strictly controlled classification system.

The differences between these two Knowledge Orders are apparent in our visualization Design vs. Emergence, Visualization of Knowledge Orders showcased in the 7th Iteration (2011): Science Maps as Visual Interfaces to Digital Libraries of Places & Spaces Exhibition/Project. We continued our work on the analysis of these classification systems individually as well, and published numerous studies on their evaluation.


A different Visualization of Wikipedia Categories and their distribution according to UDC Classes

We have developed a mixed research method strategy that was applied to the analysis of the Universal Decimal Classification (UDC) by combining web-based data collection with data and visual analyses. By evaluating the population dynamics of the UDC, we have seen ample evidence of the cultural evolution of knowledge across time. While this approach to research is important for knowledge organization as such, it also bears potential for information providers to use visualizations to showcase their collections.


Publications:

2016. Knowledge Maps of the UDC: Uses and Use Cases. A. Scharnhorst, R. P. Smiraglia, C. Guéret, A. A. Akdag Salah. Knowledge Organization, 43(8), 641-654.

Author PDF


2012. Evolution of Wikipedia’s Category Structure. K. Suchecki, A. A. Akdag Salah, C. Gao, A. Scharnhorst. Advances in Complex Systems, 15 (Supplement 1).

Published version & abstract / Author PDF


2012. The Need to Categorize: A Comparative Look at Categorization in Wikipedia and the Universal Decimal Classification System. A.A. Akdag Salah, C. Gao, K. Suchecki, A. Scharnhorst, Leonardo, 43(4).

Published version & abstract / Author PDF


2012. The evolution of classification systems: Ontogeny of the UDC. R. Smilagria, A.A. Akdag Salah, C. Gao, K. Suchecki, A. Scharnhorst. ISKO 2012.

Author PDF


2011. Visualizing Universes of Knowledge, Design and Visual Analysis of the UDC. C. v.d. Heuvel, A.A. Akdag Salah, C. Gao, K. Suchecki, A. Scharnhorst. Classification & Ontology.

Author PDF


2010. The Need to Categorize: A Comparative Look at Categorization in Wikipedia and the Universal Decimal Classification System. A.A. Akdag Salah, C. Gao, K. Suchecki, A. Scharnhorst. Satellite Meeting at ECCS.

Author PDF

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