1. Measuring Scholarly Communication on the Web Mike Thelwall Statistical Cybermetrics Research Group University of Wolverhampton, UK Bibliometric Analysis in Science and Research: Jülich 2003

2. By Olle Persson, Inforsk, Dep of Sociology Umeå, SE-901 87 Umeå

3. Contents 1. Data collection 2. Data processing 3. Analysis 4. Results

4. Why analyse scholarly communication on the Web? Ensure that the Web is efficiently used for research communication Identify trends in informal scholarly communication Suggest improvements in search tools Exploratory research: the Web is important and a valid object for scientific study

5. How can Web scholarly communication be tracked? Web server logs Good source, but restricted to individual sites Hyperlinks Secondary source of information – few users actually create hyperlinks Commercial search engines can be used for raw data about the ‘whole’ Web Analogies with bibliometric citation studies Hyperlink studies will be discussed in this article

6. Methodologies: Data collection Web crawler AltaVista advanced queries link:fz-juelich.de AND host:ac.uk AllTheWeb advanced queries Google Does not support same level of Boolean querying (even with the API)

9. Methodologies: Data processing 1 Link counts to target universities Inter-site links only Colink counts B and C are colinked Couplings D and E are coupled BC A DE F

10. Methodologies: Data processing 2 Alternative Document Models E.g. count links between domains (ignoring multiple links) instead of pages P1 P2 P3 P4 P5 P6 www.wlv.ac.uk www.albany.edu

11. Methodologies: Data analysis Statistical techniques for evaluating results Correlation with known research performance measures Factor analysis, Multi-Dimensional Scaling, Cluster analysis for patterns Simple graphical techniques Techniques from Communication Networks research / Geography

12. Results For inter-university linking, inter- departmental linking and journal Web sites

13. Inter-university links Counts of links to universities within a country can correlate significantly with measures of research productivity Geographic factors also play a part Links between universities are created for a wide range of reasons, rarely to cite research

14. Links to UK universities against their research productivity The reason for the strong correlation is the quantity of Web publication, not its quality This is different to citation analysis

15. Expected link counts against distance between UK universities

16. Link Creation Motivations For links between UK university Web sites Less than 1% equivalent to citations About 90% are related to scholarly activity, including research and teaching The rest include recreational and administrative links Link counts mainly represent a wide range of types of informal scholarly communication But not cognitive connections or online impact of the research itself It is difficult to interpret link count values

17. Journal Web Sites Can calculate Web versions of Journal Impact Factors? Web impact factors correlate with Journal Impact Factors within a discipline Also affected by Web site age and contents This is complicated by the existence of publishers’ digital libraries and digital meta-libraries

18. Disciplinary Research Linking In the US, links to chemistry and psychology departments from other departments associate with total research impact No evidence of a significant geographic trend Disciplinary differences in the extent of interlinking: history Web use is very low {Research with Rong Tang}

19. Linguistic Factors in EU Communication English the dominant language for Web sites in the Western EU In a typical country, 50% of pages are in the national language(s) and 50% in English Non-English speaking extensively interlink in English {Research with Rong Tang}

20. Mapping patterns of international communication Counts of links between Asia- Pacific universities are represented by arrow thickness. {Research with Alastair Smith, VUW, NZ}

21. The Future Mapping patterns of academic Web communication Individuals exploiting AltaVista to investigate online perceptions of their site Developing data mining tools to extract information and predict based upon link patterns Combining links with text-based approaches (Computer Science) Improved understanding of informal scholarly communication on the Web More effective use of the Web by scholars, e.g. via PhD training