That’s so Meta! Usability of a Hypergraph-Based Discussion Model

  • Felix Dietze
  • André Calero Valdez
  • Johannes Karoff
  • Christoph Greven
  • Ulrik Schroeder
  • Martina Ziefle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10287)

Abstract

Massive online communication systems such as social networks, message boards and comment sections are widely used, yet fail in conveying a diverse public opinion. Limitations of models and protocols do not allow users to precisely express their intention and to maintain a complete overview in large-scale discussions. Data-driven approaches fail as well, as they remove the nuances of human communication and use coarse representations like trends, summaries and abstract visualizations. We argue that a new discussion model and a large-scale communication protocol is needed. We evaluate the comprehensibility of a hyperedge connection in modeling arguments for online discussions. An initial mechanical turk study (\(n=200\)) revealed that 30% of the subjects intuitively considered using hyperedges. This was followed by a user study of a prototype (\(n=51\)), where 80% actively used hyperedges. Both findings were independent of user diversity factors (age, gender, graph theory knowledge). The prototypical implementation was evaluated positively.

Keywords

Online discussion systems Argument mapping Living document Hypergraph User study e-democracy 

References

  1. 1.
    Hilbert, M.: The maturing concept of e-democracy: from E-voting and online consultations to democratic value out of jumbled online chatter. J. Inf. Technol. Polit. 6(2), 87–110 (2009)CrossRefGoogle Scholar
  2. 2.
    Faridani, S., Bitton, E., Ryokai, K., Goldberg, K.: Opinion space: a scalable tool for browsing online comments. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1175–1184. ACM (2010)Google Scholar
  3. 3.
    Kunsch, D.W., Schnarr, K., van Tyle, R.: The use of argument mapping to enhance critical thinking skills in business education. J. Educ. Bus. 89(8), 403–410 (2014)CrossRefGoogle Scholar
  4. 4.
    Dwyer, C.P., Hogan, M.J., Stewart, I.: The evaluation of argument mapping as a learning tool: comparing the effects of map reading versus text reading on comprehension and recall of arguments. Think. Skills Creat. 5(1), 16–22 (2010)CrossRefGoogle Scholar
  5. 5.
    Davies, M.: Concept mapping, mind mapping and argument mapping: what are the differences and do they matter? High. Educ. 62(3), 279–301 (2011)CrossRefGoogle Scholar
  6. 6.
    Shum, S.B., De Liddo, A., Klein, M.: DCLA meet CIDA: collective intelligence deliberation analytics. In: 2nd International Workshop on Discourse-Centric Learning Analytics, LAK14: 4th International Conference on Learning Analytics & Knowledge (2014)Google Scholar
  7. 7.
    Toulmin, S.E.: The Uses of Argument. Cambridge University Press, Cambridge (1958)Google Scholar
  8. 8.
    Kunz, W., Rittel, H.W.: Issues as Elements of Information Systems, vol. 131. Institute of Urban and Regional Development, University of California Berkeley, California (1970)Google Scholar
  9. 9.
    Lee, J.: SIBYL: a tool for managing group design rationale. In: Proceedings of the 1990 ACM Conference on Computer-Supported Cooperative Work, pp. 79–92. ACM (1990)Google Scholar
  10. 10.
    Cosley, D., Frankowski, D., Terveen, L., Riedl, J.: Using intelligent task routing and contribution review to help communities build artifacts of lasting value. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1037–1046. ACM (2006)Google Scholar
  11. 11.
    Van Gelder, T.: The rationale for rationale. Law Probab. Risk 6(1–4), 23–42 (2007)CrossRefGoogle Scholar
  12. 12.
    Fu, B., Noy, N.F., Storey, M.-A.: Indented tree or graph? a usability study of ontology visualization techniques in the context of class mapping evaluation. In: Alani, H., et al. (eds.) ISWC 2013. LNCS, vol. 8218, pp. 117–134. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41335-3_8 CrossRefGoogle Scholar
  13. 13.
    Fu, B., Noy, N.F., Storey, M.A.: Eye tracking the user experience-an evaluation of ontology visualization techniques. Semantic Web (Preprint), pp. 1–19 (2015)Google Scholar
  14. 14.
    Trapani, G., Pash, A.: The Complete Guide to Google Wave. 3ones Inc., San Diego (2010)Google Scholar
  15. 15.
    Sumner, T., Shum, S.B.: From documents to discourse: shifting conceptions of scholarly publishing. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 95–102. ACM Press/Addison-Wesley Publishing Co. (1998)Google Scholar
  16. 16.
    Garcia-Castro, A., Labarga, A., Garcia, L., Giraldo, O., Montana, C., Bateman, J.A.: Semantic web and social web heading towards living documents in the life sciences. Web Semant.: Sci. Serv. Agents World Wide Web 8(2), 155–162 (2010)CrossRefGoogle Scholar
  17. 17.
    Whittaker, S., Terveen, L., Hill, W., Cherny, L.: The dynamics of mass interaction. In: Lueg, C., Fisher, D. (eds.) From Usenet to CoWebs, pp. 79–91. Springer, London (2003)CrossRefGoogle Scholar
  18. 18.
    Odersky, M., Altherr, P., Cremet, V., Emir, B., Maneth, S., Micheloud, S., Mihaylov, N., Schinz, M., Stenman, E., Zenger, M.: An overview of the scala programming language. Technical report (2004)Google Scholar
  19. 19.
    Dietze, F., Karoff, J., Calero Valdez, A., Ziefle, M., Greven, C., Schroeder, U.: An open-source object-graph-mapping framework for Neo4j and scala: renesca. In: Buccafurri, F., Holzinger, A., Kieseberg, P., Tjoa, A.M., Weippl, E. (eds.) CD-ARES 2016. LNCS, vol. 9817, pp. 204–218. Springer, Cham (2016). doi:10.1007/978-3-319-45507-5_14 CrossRefGoogle Scholar
  20. 20.
    Bostock, M., Ogievetsky, V., Heer, J.: D\(^3\) data-driven documents. IEEE Trans. Visual. Comput. Graph. 17(12), 2301–2309 (2011)CrossRefGoogle Scholar
  21. 21.
    Sauro, J.: Sustisfied? little-known system usability scale facts. UX Mag. 10(3), 2011–2013 (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Felix Dietze
    • 1
  • André Calero Valdez
    • 1
  • Johannes Karoff
    • 1
  • Christoph Greven
    • 2
  • Ulrik Schroeder
    • 2
  • Martina Ziefle
    • 1
  1. 1.Human-Computer Interaction CenterRWTH Aachen UniversityAachenGermany
  2. 2.Learning Technologies GroupRWTH Aachen UniversityAachenGermany

Personalised recommendations