Exploring Emerging Topics in Social Informatics: An Online Real-Time Tool for Keyword Co-Occurrence Analysis

  • Florian Cech
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10540)


In an academic field as diverse as Social Informatics, identifying current and emergent topics presents a significant challenge to individuals and institutions alike. Several approaches based on keyword assignment and visualizing co-occurrence networks have already been described with the goal of providing insight into topical and geographical clusters of publications, authors or institutions. This work identifies a few key challenges to the aforementioned methods and proposes an interdisciplinary approach based on qualitative text analysis to assign keywords to research institutions and quantitatively explore them by building interactive co-occurrence and research focus parallelship networks. The proposed technique is then applied to the field of Social Informatics by identifying more than a hundred organizations worldwide within that domain, coding them with keywords based on research group titles, online self-descriptions and affiliated publications, and creating an online tool to generate interactive co-occurrence, network neighbourhood and research focus parallelship visualizations.


Keyword visualization Co-Occurrence networks Neighbourhood analysis Research focus parallelship Scientometrics Social informatics 


  1. 1.
    Abello, J., Van Ham, F., Krishnan, N.: ASK-graphview: a large scale graph visualization system. IEEE Trans. Visual. Comput. Graphics 12(5), 669–676 (2006)CrossRefGoogle Scholar
  2. 2.
    Barnes, J., Hut, P.: A hierarchical o(n log n) force-calculation algorithm. Nature 324(6096), 446–449 (1986)CrossRefGoogle Scholar
  3. 3.
    Bastian, M., Heymann, S., Jacomy, M.: An open source software for exploring and manipulating networks. In: Third International AAAI Conference on Weblogs and Social Media, pp. 361–362 (2009)Google Scholar
  4. 4.
    Bhattacharya, S., Basu, P.: Mapping a research area at the micro level using co-word analysis. Scientometrics 43, 359–372 (2006)CrossRefGoogle Scholar
  5. 5.
    Brass, D., Burkhardt, M.E.: Centrality and power in organizations. In: Networks and Organizations: Structure, Form, and Action, pp. 191–215 (1992)Google Scholar
  6. 6.
    Brown, K.R., Otasek, D., Ali, M., McGuffin, M.J., Xie, W., Devani, B., van Toch, I.L., Jurisica, I.: NAViGaTOR: network analysis, visualization and graphing Toronto. Bioinformatics 25(24), 3327–3329 (2009)CrossRefGoogle Scholar
  7. 7.
    Cambrosio, A., Keating, P., Mercier, S., Lewison, G., Mogoutov, A.: Mapping the emergence and development of translational cancer research. Eur. J. Cancer 42(18), 3140–3148 (2006)CrossRefGoogle Scholar
  8. 8.
    Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)CrossRefGoogle Scholar
  9. 9.
    Goebel, R.: Lecture Notes in Artificial Intelligence Subseries of Lecture Notes in Computer Science LNAI Series Editors (2011)Google Scholar
  10. 10.
    Hu, M., Wongsuphasawat, K., Stasko, J.: Visualizing social media content with sententree. IEEE Trans. Vis. Comput. Graphics 23(1), 621–630 (2017)CrossRefGoogle Scholar
  11. 11.
    Hu, Z., Mellor, J., Wu, J., DeLisi, C.: VisANT: an online visualization and analysis tool for biological interaction data. BMC Bioinformatics 5, 17–17 (2004)CrossRefGoogle Scholar
  12. 12.
    Jacomy, M., Venturini, T., Heymann, S., Bastian, M.: ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi Software (2014)Google Scholar
  13. 13.
    Kushima, M., Araki, K., Suzuki, M., Araki, S., Nikama, T.: Graphic visualization of the co-occurrence analysis network of lung cancer in-patient nursing record. In: 2010 International Conference on Information Science and Applications, pp. 1–8. IEEE (2010)Google Scholar
  14. 14.
    Lee, P.C., Su, H.N.: Investigating the structure of regional innovation system research through keyword co-occurrence and social network analysis. Innov.: Manage., Policy Pract. 12(1), 26–40 (2010)Google Scholar
  15. 15.
    Lee, W.H.: How to identify emerging research fields using scientometrics: an example in the field of information security. Scientometrics 76(3), 503–525 (2008)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Lehmann, S., Schwartz, M., Hansen, L.K.: Biclique communities. Phys. Rev. E 78(1), P09008–9 (2008)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Leicht, E.A., Holme, P., Newman, M.E.J.: Vertex similarity in networks., (2):P10012 (2005)Google Scholar
  18. 18.
    Mayring, P.: Qualitative Inhaltsanalyse (2010)Google Scholar
  19. 19.
    Melin, G., Persson, O.: Studying research collaboration using co-authorships. Scientometrics 36(3), 363–377 (1996)CrossRefGoogle Scholar
  20. 20.
    Pereira, C.: Informatics education in Europe: Institutions, degrees, students, positions, salaries. Technical report, Informatics Europe, Zrich (2016)Google Scholar
  21. 21.
    Persson, O., Beckmann, M.: Locating the network of interacting authors in scientific specialties. Scientometrics 33(3), 351–366 (1995)CrossRefGoogle Scholar
  22. 22.
    Peters, H.P.F., Vanraan, A.F.J.: Co-word-based science maps of chemical-engineering 1. representations by direct multidimensional-scaling. Res. Policy 22(1), 47–71 (1993)CrossRefGoogle Scholar
  23. 23.
    Reh, A., Gusenbauer, C., Kastner, J., Groller, M.E., Heinzl, C.: MObjects-a novel method for the visualization and interactive exploration of defects in industrial XCT data. IEEE Trans. Vis. Comput. Graphics 19(12), 2906–2915 (2013)CrossRefGoogle Scholar
  24. 24.
    Rip, A., Courtial, J.P.: Co-word maps of biotechnology: an example of cognitive scientometrics. Scientometrics 6(6), 381–400 (1984)CrossRefGoogle Scholar
  25. 25.
    Roy, S.: Effectiveness of JavaScript graph visualization libraries in visualizing gene regulatory networks (GRN) (2015)Google Scholar
  26. 26.
    Siddiqi, S., Sharan, A.: Keyword and keyphrase extraction techniques: a literature review. Int. J. Comput. Appl. 109(2), 18–23 (2015)Google Scholar
  27. 27.
    Small, H.: Visualizing science by citation mapping. J. Am. Soc. Inf. Sci. 50(9), 799–813 (1999)CrossRefGoogle Scholar
  28. 28.
    Su, H.N., Lee, P.C.: Knowledge map of publications in research policy. In: PICMET: Portland International Center for Management of Engineering and Technology, Proceedings, pp. 2507–2516 (2009a)Google Scholar
  29. 29.
    Su, H.-N., Lee, P.-C.: Knowledge map of publications in research policy. In: PICMET 2009 - 2009 Portland International Conference on Management of Engineering & Technology, pp. 2507–2516. IEEE (2009b)Google Scholar
  30. 30.
    Su, H.N., Lee, P.C.: Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in technology foresight. Scientometrics 85(1), 65–79 (2010)CrossRefGoogle Scholar
  31. 31.
    Wang, H., Azuaje, F., Black, N.: An integrative and interactive framework for improving biomedical pattern discovery and visualization. IEEE Trans. Inf. Technol. Biomed. 8(1), 16–27 (2004)CrossRefGoogle Scholar
  32. 32.
    Wu, W., Xu, J., Zeng, H., Zheng, Y., Qu, H., Ni, B., Yuan, M., Ni, L.M.: TelCoVis: Visual exploration of co-occurrence in urban human mobility based on Telco data. IEEE Trans. Vis. Comput. Graphics 22(1), 935–944 (2016)CrossRefGoogle Scholar
  33. 33.
    Zhu, L., Liu, X., He, S., Shi, J., Pang, M.: Keywords co-occurrence mapping knowledge domain research base on the theory of big data in oil and gas industry. Scientometrics 105(1), 249–260 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Centre for Informatics and SocietyTU WienViennaAustria

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