The Range of Webometrics: Forms of Digital Social Utility as Tools

1. The Range of Webometrics: Forms of Digital Social Utility as Tools Professor Peter Ingwersen, Ph.D. Information Interaction & Information Architecture Royal School of LIS, Denmark pi@iva.dk - http://www.iva.dk/pi

2. Table of Contents • Webometrics – Cybermetrics – a framework • Link topology & structural conceptions in webometrics • Overview of potentials • Search engine analysis • Link analyses – Web Impact Factor (Web-IF) • Dataset Usage Indicators • Web mining – Trend analyses (blog contents) • Concluding remarks Ingwersen

3. Webometrics • The study of quantitative aspects of the construction and use of information resources, structuresand technologieson the Web, drawing on bibliometric and informetric methods • search engine performance • link structures, e.g., WIFs, cohesiveness of link topologies, etc. • users’ information behaviour (searching, browsing, etc.) • web page contents – knowledge mining – blog trends • Dataset analyses & impact • cybermetrics: quantitative studies of the whole Internet • i.e. chat, mailing lists, news groups, MUDs, etc. - and WWW Ingwersen • Lennart Björneborn 2001

4. L. Björneborn & P. Ingwersen 2003 infor-/biblio-/sciento-/cyber-/webo-/metrics informetrics bibliometrics scientometrics cybermetrics webometrics Ingwersen

5. corona model (Björneborn 2004) SCC Strongest Connected Component OUT reachable from SCC IN traversable to SCC Disconnected IN-Tendrils connected from IN Tube connecting IN to OUT OUT-Tendrils connected to OUT Ingwersen

6. Source: www.cybergeography.org Ingwersen

7. L. Björneborn & P. Ingwersen 2003 Link terminologybasic concepts A B E G C D F • B has an outlink to C; outlinking : ~ reference • B has an inlink from A; inlinked : ~ citation • B has a selflink; selflinking : ~ self-citation • A has no inlinks; non-linked: ~ non-cited • E and F are reciprocally linked • A is transitively linked with H via B – DH is reachable from A by a directed link path • A has a transversal link to G : short cut • C and D are co-linked from B, i.e. have co-inlinks orshared inlinks: co-citation • B and E are co-linking to D, i.e. have co-out-links orshared outlinks: bibliog.coupling H co-links Ingwersen

8. d b a c Levels of web nodes • Lennart Björneborn 2002 • 3 basic levels of web nodes: pages , sites, TLDs • different levels of selflinks and outlinks • a = page selflink • b = page outlink andsite selflink • c = site outlink and TLD selflink • d = TLD outlink • more levels: frames (page sections), sub-sites, sub-TLDs ... Ingwersen

9. Search engine analyses • See e.g. Judith Bar-Ilan’s excellent longitudinal analyses • Mike Thelwall et al. in several case studies • Scientific material on the Web: • Lawrence & Giles (1999):approx. 6 % of Web sites contains scientific or educational contents • Increasingly:the Web is a web of uncertainty • Allen et al. (1999) – biology topics from 500 Web sites assessed for quality: • 46 % of sites were ”informative” – but: • 10-35 % inaccurate; 20-35 % misleading • 48 % unreferenced Ingwersen

10. http://searchenginewatch.com/3634992 Ingwersen

11. Ingwersen

12. www.internetworldstats.com 12 Ingwersen Knoxville 2010

13. Possible types of Web-IF: E-journal Web-IF Calculated by in-links Calculated as traditional JIF (citations) Scientific web site – IF (by link analyses) National – regional (some URL-problems in TDL) Institutions – single sites Other entities, e.g. domains Best nominator:no. of staff, beds – or simply use external inlinks (Thelwall et al., 2002) Blog IF: no. of external inlinks / blog entries Twitter IF: no of external inlinks / twitter entries (Holmberg, 2009) 13 Ingwersen Knoxville 2010

14. The only valid webometric tool: Site Explorer Yahoo Search … • If one enters (old valid) commands like: • Link:URL or Domain: topdomain (edu, dk) or Site:URL you are transferred to: http://siteexplorer.search.yahoo.com/new/ • Or find it via this URL • The same facilities are available in click-mode, as one starts with a given URL: • Finding ‘all’ web pages in a site • Finding ‘all’ inlinks to that site/those pages • Also without selflinks! – this implies … Ingwersen

15. … to calculate Web Impact Factors • But one should be prudent in interpretations. • Note that external inlinks is the best indicator of recognition (see sample) • Take care of how many sub-domains (and pages) that are included in the click analysis. • Results can be downloaded Ingwersen

16. Consequences for Yahoo Site Expl. Take care on which domain-level you are: www.yahoo.com does not contain sub-domains like maps.yahoo.com – only those below its name directly. Yahoo.com will thus contain maps… Also beware of the path structure Minor tests show that probably the inlink no. really implies inlinks – not inlinking web pages. Ingwersen 16 2010 Åbo

17. Search sample: www.db.dk/pi Ingwersen

18. Without selflinks … Ingwersen

19. The Web-Impact Factor Ingwersen, 1998 • Intuitively (naively?) believed as similar to the Journal Impact Factor • Demonstrate recognition by other web sites - or simply impact – notnecessarilyquality • Central issue: are web sites similar to journals and web pages similar to articles? • Are in-links similar to citations – or simply road signs? • What is really calculated? • DEFINE WHAT YOU ARE CALCULATING: site or page IF Ingwersen

20. Web-links like citations? • Kleinberg (1998) between citation weights and Google’s PageRank: Hubs~ review article: have many outlinks (refs) to: Authority pages~ influential (highly cited) documents: have many inlinks fromHubs! Typical: Web index pages =homepage with self-inlinks = Table of contents Ingwersen

21. Reasons for outlinking … • Out-links mainly for functional purposes • Navigation – interest spaces… • Pointing to authority in certain domains? (Latour:rhetoric reasons for references-links) • Normative reasonsfor linking? (Merton) • Do we have negative links? • We do have non-linking (commercial sites) Ingwersen

22. Some additional reasons for providing links In part analogous to providing references (recognition) And, among others, • emphasising the own position and relationship (professional, collaboration, self-presentation etc.) • sharing knowledge, experience, associations … • acknowledging support, sponsorship, assistance • providing information for various purposes (commercial, scientific, education, entertainment) • drawing attention to questions of individual or common interest and to information provided by others (the navigational purpose) Ingwersen

23. Other differences between references, citations & links • The time issue: • Agingof sources are different on the Web: • Birth, Maturity & Obsolescence happens faster • Decline & Death of sources occur too– but • Mariages – Divorse – Re-mariage – Death & Resurrection…& alike liberalphenomena are found on the Web! (Wolfgang Glänzel) Ingwersen

24. Dataset usage indicators: a novelwebometric approach • Biodiversity datasets are: • Searchable • Downloadable … in • Open access • See e.g. GBIF websiteand 2009 publication:Vishwas S Chavan and Peter Ingwersen, BMC Bioinformatics, 2009, 10(Suppl 14):S2 Ingwersen

25. Example:Denmark – GBIF dataset providers • DanBioInfoFacility – many datasets • HerbariumUA: only two datasets • Comparable US dataset provider: • OBIS – Ocean Bio Info System Ingwersen

26. DanBIF distribution of datasets – sampleselectionsorted by Search Events Ingwersen, P. & Vishwas, C. (under review): INDICATORS FOR A DATA USAGE INDEX: AN INCENTIVE FOR PUBLISHING PRIMARY BIODIVERSITY DATA THROUGH AGLOBAL INFORMATION INFRASTRUCTURE.BMC Bioinformatics. Ingwersen

27. Sample of Dataset Usage Indicators (DUI) Ingwersen

28. Issue tracking – Web mining • Adequate sampling requires knowledge of the structure and properties of the population- the Web space to be sampled • Issue trackingof known properties / issues may help • Web mining the unknown is more difficult, due to • the dynamic, distributed & diverse nature • the variety of actors and minimum of standards • the lack of quality control of contents • Web archeology – study of the past Web Ingwersen

29. Nielsen Blog Pulse – social utility indicator • Observes blogs worldwide by providing: • Trend search– development over time of terms/concepts – user selection! • Featured trends– predefined categories • Coversation tracker– blog conversations • BlogPulse profiles– blog profiles • Look into: http://www.blogpulse.com/tools.html Ingwersen

30. Home > ToolsTrend Search Ingwersen

31. Informetric methods useful • Co-occurrence analyses (terms; names…) • Co-link and co-linking analyses • Bradford-like (skewed) distributions of links probably found in sectors of web space • In order to define the strong ties…between top frequency web objects in two sectors of topical difference • Weak (low frequency) ties – Small-Worlds – Serendipity between objects in the two sectors: UNEXPECTED relations may occur Ingwersen

32. Source: www.cybergeography.org Weak Tie! Ingwersen

33. Concluding remarks • One may be somewhat cautious on Web-IF applications without careful sampling via robotsdue to its incomprehensiveness and what it actually signifies • One might also try to investigate more the behavioural aspects of providing and receiving linksto understand what the impact might mean and how/why links are made • Better to understand the Web space information structure • Design workable robots, downloading & local analyses • Move into the social media and open access genres with social utility indicators Ingwersen

34. Concluding remarks - 2 • Issue tracking and web mining are:applications of Web IR / Webometrics • Combined IR and informetric methods seem promissing: • co-occurrence analyses – mapping - clustering • co-links and co-linking - transversal links • Knowledge discovery and use in diversified web spaces Ingwersen

35. Additional References • Adamic, L. (1999). The small world Web. Lecture Notes in Computer Science, 1696: 443-452. • Almind, T.C. And Ingwersen, P. Informetric analyses on the World Wide Web: Methodological approaches to ”Webometrics”. Journal of Documentation, 53 (1997), 404-426. • Björneborn, L. (2001). Small-world linkage and co-linkage. Proceedings of the 12th ACM Conference on Hypertext, pp. 133-134. • Björneborn, L. & Ingwersen, P. (2001). Perspectives of webometrics. Scientometrics, 50(1): 65-82. • Björneborn, L. & Ingwersen, P. (2004). Towards a basic framework of webometrics. (submitted) • Broder, A. et al. (2000). Graph structure in the Web. Computer Networks, 33(1-6): 309-320. • Chakrabarti, S. et al. (1999). Mining the Web’s link structure. IEEE Computer, 32(8): 60-67. • Chavan, Vishwas S. & Ingwersen, P. (2009). Towards a data publishing framework for primary biodiversity data. BMC Bioinformatics, 10(Supp. 14): S2 • Granovetter, M.S. (1973). The strength of weak ties. American Journal of Sociology, 78(6): 1360-1380. Ingwersen

36. References -2 • Ingwersen, P. The calculation of Web Impact Factors. Journal of Documentation, 54 (1998), 236-243 • Kousha, K. & Thelwall, M. (2007). How is Science cited on the Web? A classification of Google unique Web citations. Journal of American Society for Information Science and Technology, 58(11): 1631-1644. • Matthews, R. (1998). Six degrees of separation. New Scientist, June 6. • Newman, M.E.J. (2001). The structure of scientific collaboration networks. PNAS, 98(2): 404-409. • Rousseau, R. Daily time series of common single word searches in AltaVista and Northern Light. Cybermetrics, 2/3, paper 2. ISSN: 1137-5019. (http://www.cindoc.csic.es/cybermetrics/articles/v2ilp2.html) • Small, H. (1999). A passage through science: crossing disciplinary boundaries. Library Trends, 48(1): 72-108. • Swanson, D.R. (1986). Undiscovered public knowledge. Library Quarterly, 56(2): 103-118. • Thelwall, M. Web impact factors and search engine coverage. Journal of Documentation, 56 (2000), 185-189. • Watts, D. J. & Strogatz, S.H.(1998). Collective dynamics of ‘small-world’ networks. Nature, 393 (June 4): 440-442. Ingwersen