Indicators from the web - making the invisible visible?

Transcription

1 Indicators from the web - making the invisible visible? Presentation given at the workshop The World Wide Web and Access to Knowledge Workshop, February 9-10, 2006, Oxford Andrea Scharnhorst Royal Netherlands Academy of Arts and Sciences Virtual Knowledge Studio

2 Web indicators are interesting, possible - but complicated

3 Content Target of web indicators Timeline of web indicator research Some results from WISER Data a moving target The web as a complex network Visualizing the European Research Area Summary / Perspectives

4 search for Nerdi,Amsterdam August 2002

5 Target of web indicators Understanding science: knowledge practices and social processes in science, in particular in e-science Developing evaluation tools for e-science for science policy

6 Content Target of web indicators Timeline of web indicator research Some results from WISER Data a moving target The web as a complex network Visualizing the European Research Area Summary / Perspectives

7 Timeline of web indicator research Statistical Indicators Benchmarking the Information Society ( ) Cybermetrics Session and journal Almind, Ingwersen webometrics Bray measuring the web Rousseau Ingwersen sitations WIF Web Indicators for Scientific, Technological and Innovation Research ( ) Scientometrics Vol 50 European Indicators, Cyberspace and the Science-Technology-Economy System ( ) JASIST Vol 55(14) Web technologies Web statistics Information society and politics of the Internet

8 Internet studies (in economics, sociology, political sciences, library and information sciences,...) Qualitatively oriented Timeline of web indicator research AoIR (1999/2001) Web analysis (hosts, domains, traffic,...) Quantitatively oriented Virtual society? Web Indicators Atlas of Cybergeography Dodge, Kitchin 2001 Laws of the web, Internet ecologies Huberman 2001 Web as a complex network non-linear mathematical models (traffic, growth, structure)

9 Content Target of web indicators Timeline of web indicator research Some results from WISER Data a moving target The web as a complex network Visualizing the European Research Area Summary / Perspectives

10 WISER Data a moving target Giant Weakly Connected Component Giant In Component Tube Giant Out Component Disconnected Components Giant Strongly Connected Component Different Crawls -> Different Graphs - directedness - crawl parameter - temporal changes

11 WISER Data a moving target New technological developments Ortega, Aguillo, Prieto (2006) Longitudinal Study of Contents and Elements in the Scientific Web environment, Journal of Information Science

12 WISER Data a moving target Representative on a statistical level Reference system(s) for test crawling Web archiving (saving the past) Time series

13 Content Target of web indicators Timeline of web indicator research Some results from WISER Data a moving target The web as a complex network Visualizing the European Research Area Summary / Perspectives

14 D.J. Watts, Small Worlds: The Dynamics of Networks Between Order and Randomness, 1999; S. Bornholdt, H.G. Schuster (Eds): Handbook of Graphs and Networks, 2003; N. Dorogovtsev, J.F.F. Mendes, Evolution of Networks:From biological Nets to the Internet, 2003; R. Pastor-Satorras, A. Vespignani, Evolution and Structure of the Internet, 2004 M. Buchanan, Nexus: Small Worlds and the Groundbreaking Science of Networks, 2002; B.A. Huberman, The Laws of the Web: Patterns in the Ecology of Information, 2001; A.-L. Barabasi, Linked: The New Science of Networks, 2002 ; D.J. Watts, Six Degrees: The Science of a Connected Age, 2003 Scharnhorst, A.(2003) "Complex Networks and the Web: Insights from Nonlinear Physics", Journal of Computer-Mediated Communication 8(4) WISER The web as a complex network WoS, Complex networks Number of documents Years

15 WISER The web as a complex network - small world property: the average separation between nodes is rather small - the degree of clustering is higher then expected from random networks (cliques and highly connected subgraphs = web communities) - the degree distribution is scale free Barabasi et al. 2002, Physica A 311, P(k) ~ C / (k 0 +k) γ

16 WISER The web as a complex network FRA GER LUX Cothey, Viv (2005) Some preliminary results from a link-crawl of the European Union Research Area Web. In: P. Ingwersen, B. Larsen (Eds), Proceedings of the ISSI 2005, pp

17 WISER The web as a complex network "Country" indegree national indegree variance (actual) pages (expected) (actual:expected) (.fr) France (.es) Spain (.de) Germany (.ie) Ireland (.nl) The Neth (.gr) Greece (.be) Belgium (.uk) UK (.lu) Luxembourg (.it) Italy (.at) Austria (.dk) Denmark (.fi) Finland (.se) Sweden (.pt) Portugal Scale adjusted Web recognition factor

18 WISER The web as a complex system Scale free indicators (Katz) or benchmarking indicators (Thelwall) Exploring the possibilities to use other characteristics of complex systems (clustering coefficient) Use mathematical models of web growth as heuristic tools for quant/qualitative research into the mechanism of collective behavior creating the self-organized (academic) web (dynamic observations vs. static snapshots)

19 Content Target of web indicators Timeline of web indicator research Some results from WISER Data a moving target The web as a complex network Visualizing the European Research Area Summary / Perspectives

20 WISER Visualizing the European Research Area Jose Luis Ortega -correlation between size and in-links, geography matters

21 WISER Visualizing the European Research Area Jose Luis Ortega Coexistence between national reference systems and the ERA

22 utch national network : in-links dentify the intermediares in nternational networks WISER Visualizing the European Research Area Jose Luis Ortega

23 Content Target of web indicators Timeline of web indicator research Some results from WISER Data a moving target The web as a complex network Visualizing the European Research Area Summary / Perspectives

24 Summary Web indicators are snapshots of dynamic representations of scholarly activity They differentiate between the dynamics of the representations and the dynamics of the (multi)media in which the representations manifest themselves The data collection process for Web indicators requires a combination of automated data mining (crawler, search engines) and human efforts, thus mixing quantitative and qualitative methodologies Web indicators require a model of the purpose and function of the web activity of scholars

25 Perspectives Standardization: Link repositories, web archiving, web-isi Comparison: Search engines and tool development Crossing disciplinary boundaries Bibliomining (Nicholson) / CiteBase (Open Citation Project) / Social science link analysis (Thelwall) Artificial societies and simulated indicators (Leydesdorff/Scharnhorst)

26 Web indicators are interesting, possible - and complicated

27 Acknowledgement This work was supported by a grant from the Common Basis for Science, Technology and Innovation Indicators part of the Improving Human Research Potential specific programme of the Fifth Framework for Research and Technological Development of the European Commission. It is part of the WISER project (Web indicators for scientific, technological and innovation research) (Contract HPV2-CT ) ( WISER consortium Co-ordinator