What am I not Seeing? An Interactive Approach to Social Content Discovery in Microblogs

  • Byungkyu Kang
  • Nava Tintarev
  • Tobias Höllerer
  • John O’Donovan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10047)

Abstract

In this paper, we focus on the informational and user experience benefits of user-driven topic exploration in microblog communities, such as Twitter, in an inspectable, controllable and personalized manner. To this end, we introduce “HopTopics” – a novel interactive tool for exploring content that is popular just beyond a user’s typical information horizon in a microblog, as defined by the network of individuals that they are connected to. We present results of a user study (N=122) to evaluate HopTopics with varying complexity against a typical microblog feed in both personalized and non-personalized conditions. Results show that the HopTopics system, leveraging content from both the direct and extended network of a user, succeeds in giving users a better sense of control and transparency. Moreover, participants had a poor mental model for the degree of novel content discovered when presented with non-personalized data in the Inspectable interface.

Keywords

Communities Content discovery Explanations Interfaces Microblogs Visualization 

References

  1. 1.
    Amar, R.A., Stasko, J.T.: Knowledge precepts for design and evaluation of information visualization. IEEE Trans. Vis. Comput. Graph. 11, 432–442 (2005)CrossRefGoogle Scholar
  2. 2.
    André, P., Schraefel, M.C., Teevan, J., Dumais, S.T.: Discovery is never by chance: Designing for (un)serendipity. In: Creativity and Cognition (2009)Google Scholar
  3. 3.
    Bennett, S.W., Scott, A.C.: The rule-based expert systems: The MYCIN experiments of the stanford heuristic programming project, chapter 19 - specialized explanations for dosage selection, pp. 363–370. Addison-Wesley Publishing Company (1985)Google Scholar
  4. 4.
    Bernstein, M.S., Suh, B., Hong, L., Chen, J., Kairam, S., Chi, E.H.: Eddi: Interactive topic-based browsing of social status streams. In: User Interface Software and Technology, UIST 2010, pp. 303–312 (2010)Google Scholar
  5. 5.
    Broersma, M., Graham, T.: Twitter as a news source. J. Pract. 7(4), 446–464 (2013)Google Scholar
  6. 6.
    Brusilovsky, P., Schwarz, E., Weber, G.: ELM-ART: an intelligent tutoring system on world wide web. In: Frasson, C., Gauthier, G., Lesgold, A. (eds.) ITS 1996. LNCS, vol. 1086, pp. 261–269. Springer, Heidelberg (1996). doi:10.1007/3-540-61327-7_123 CrossRefGoogle Scholar
  7. 7.
    Cerutti, F., Tintarev, N., Oren, N.: Formal arguments, preferences, and natural language interfaces to humans: an empirical evaluation. In: ECAI, pp. 207–212 (2014)Google Scholar
  8. 8.
    Dimitrova, V.: Style-olm: Interactive open learner modelling. Int. J. Artif. Intell. Educ. 17(2), 35–78 (2003)Google Scholar
  9. 9.
    Garcia Esparza, S., O’Mahony, M.P., Smyth, B.: Catstream: Categorising tweets for user profiling and stream filtering. In: International Conference on Intelligent User Interfaces, IUI 2013, pp. 25–36 (2013)Google Scholar
  10. 10.
    Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)CrossRefGoogle Scholar
  11. 11.
    Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining collaborative filtering recommendations. In: ACM Conference on Computer Supported Cooperative Work, pp. 241–250 (2000)Google Scholar
  12. 12.
    Herlocker, J.L., Konstan, J.A., Terveen, L., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRefGoogle Scholar
  13. 13.
    Knijnenburg, B.P., Willemsen, M.C., Gantner, Z., Soncu, H., Newell, C.: Explaining the user experience of recommender systems. User Model. User Adap. Inter. 22(4–5), 441–504 (2012)CrossRefGoogle Scholar
  14. 14.
    Kulesza, T., Burnett, M., Wong, W.-K., Stumpf, S.: Principles of explanatory debugging to personalize interactive machine learning. In: IUI (2015)Google Scholar
  15. 15.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is twitter, a social network or a news media? In: International Conference on World Wide Web, WWW 2010, pp. 591–600 (2010)Google Scholar
  16. 16.
    Marcus, A., Bernstein, M.S., Badar, O., Karger, D.R., Madden, S., Miller, R.C.: Twitinfo: Aggregating and visualizing microblogs for event exploration. In: Conference on Human Factors in Computing Systems, CHI 2011, pp. 227–236 (2011)Google Scholar
  17. 17.
    Nagulendra, S., Vassileva, J.: Providing awareness, understanding and control of personalized stream filtering in a P2P social network. In: Antunes, P., Gerosa, M.A., Sylvester, A., Vassileva, J., Vreede, G.-J. (eds.) CRIWG 2013. LNCS, vol. 8224, pp. 61–76. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41347-6_6 CrossRefGoogle Scholar
  18. 18.
    Paramythis, A., Weibelzahl, S., Masthoff, J.: Layered evaluation of interactive adaptive systems: Framework and formative methods. User Model. User Adap. Interact. 20, 2–12 (2010)Google Scholar
  19. 19.
    Pariser, E.: The filter bubble: What the Internet is hiding from you. Penguin Books, New York (2011)Google Scholar
  20. 20.
    Schaffer, J., Giridhar, P., Jones, D., Höllerer, T., Abdelzaher, T., O’Donovan, J.: Getting the message?: A study of explanation interfaces for microblog data analysis. In: Intelligent User Interfaces, IUI 2015, pp. 345–356 (2015)Google Scholar
  21. 21.
    Sharma, A., Cosley, D.: Do social explanations work? studying and modeling the effects of social explanations in recommender systems. In: World Wide Web (WWW) (2013)Google Scholar
  22. 22.
    Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001). doi:10.1007/3-540-44593-5_25 CrossRefGoogle Scholar
  23. 23.
    Teevan, J., Morris, M.R., Azenkot, S.: Supporting interpersonal interaction during collaborative mobile search. Computer 47(3), 54–57 (2014)CrossRefGoogle Scholar
  24. 24.
    Tintarev, N., Kang, B., ODonovan, J.: Inspection mechanisms for community-based content discovery in microblogs. In: IntRS15 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems at ACM Recommender Systems (2015)Google Scholar
  25. 25.
    Tintarev, N., Masthoff, J.: Recommender Systems Handbook (second ed.), chapter Explaining Recommendations: Design and Evaluation (2015)Google Scholar
  26. 26.
    Verbert, K., Parra, D., Brusilovsky, P., Duval, E.: Visualizing recommendations to support exploration, transparency and controllability. In: International Conference on Intelligent User Interfaces, IUI 2013, pp. 351–362 (2013)Google Scholar
  27. 27.
    Wang, B., Ester, M., Bu, J., Cai, D.: Who also likes it? generating the most persuasive social explanations in recommender systems. In: AAAI Conference on Artificial Intelligence (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Byungkyu Kang
    • 1
  • Nava Tintarev
    • 2
  • Tobias Höllerer
    • 1
  • John O’Donovan
    • 1
  1. 1.Department of Computer ScienceUniversity of CaliforniaSanta BarbaraUSA
  2. 2.Department of Informatics and ComputingBournemouth UniversityPooleUK

Personalised recommendations