Abstract
In Online Social Networks (OSNs) users are often overwhelmed with the huge amount of social data, most of which are irrelevant to their interest. Filtering of the social data stream is the way to deal with this problem, and it has already been applied by OSNs, such as Facebook. Unfortunately, personalized filtering leads to “the filter bubble” problem where the user is trapped inside a world within the limited boundaries of her interests and cannot be exposed to any surprising, desirable information. Moreover, these OSNs are black boxes, providing no transparency of how the filtering mechanism decides what is to be shown in the social data stream. As a result, the user trust in the system can decline. This paper proposes an interactive method to visualize the personalized stream filtering in OSNs. The proposed visualization helps to create awareness, understanding, and control of personalized stream filtering to alleviate “the filter bubble” problem and increase the users’ trust in the system. The visualization is implemented in MADMICA – a privacy aware decentralized OSN, based on the Friendica P2P protocol. We present the results of a small-scale study to evaluate the user experience with the proposed visualization in MADMICA.
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References
Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)
Garrett, J.J.: The Elements of User Experience: User-Centered Design for the Web and Beyond. Pearson Education (2010)
Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Proc. ACM Conference on Computer Supported Cooperative Work, CSCW 2000, pp. 241–250. ACM Press, New York (2000)
Johnson, H., Johnson, P.: Explanation Facilities and Interactive Systems. In: Proc. 1st International Conference on Intelligent User Interfaces, IUI 1993, pp. 159–166. ACM (1993)
Kaela, S.: The Role of HTML5 and Flash in Web Design (2012)
Konstan, J.A., Riedl, J.: Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction 22(1-2), 101–123 (2012)
Macgirvin, M.: DFRN – the Distributed Friends & Relations Network, https://macgirvin.com/spec/dfrn2.pdf (accessed August 2, 2012)
Nagulendra, S., Vassileva, J.: Minimizing Social Data Overload through Interest-Based Stream Filtering in a P2P Social Network. To appear in Proc. of the IEEE International Conference on Social Computing, SocialCom 2013 (2013)
Pariser, E.: The Filter Bubble: What the Internet Is Hiding from You. Penguin Press HC (2011)
Pu, P., Chen, L.: Trust Building with Explanation Interfaces. In: Proc. 11th International Conference on Intelligent User Interfaces, IUI 2006, p. 93. ACM Press, New York (2006)
Resnick, P., Munson, S.A., Garrett, R.K., Stroud, N.J., Kriplean, T.: Bursting Your (Filter) Bubble: Strategies for Promoting Diverse Exposure. In: Proc. of the Conference on Computer Supported Cooperative Work, CSCW 2013 Companion Proc., pp. 95–100. ACM (2013)
Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 56–58 (1997)
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.) Recommender Systems Handbook, pp. 1–35. Springer, US (2011)
Shneiderman, B.: The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations. In: Proc. 1996 IEEE Symposium on Visual Languages, pp. 336–343. IEEE Comput. Soc. Press (1996)
Tandukar, U., Vassileva, J.: Selective Propagation of Social Data in Decentralized Online Social Network. In: Ardissono, L., Kuflik, T. (eds.) UMAP 2011 Workshops. LNCS, vol. 7138, pp. 213–224. Springer, Heidelberg (2012)
Tandukar, U., Vassileva, J.: Ensuring Relevant and Serendipitous Information Flow in Decentralized Online Social Network. In: Ramsay, A., Agre, G. (eds.) AIMSA 2012. LNCS (LNAI), vol. 7557, pp. 79–88. Springer, Heidelberg (2012)
Tintarev, N., Masthoff, J.: Effective Explanations of Recommendations: User-Centered Design. In: Proc. ACM Conference on Recommender Systems, RecSys 2007, pp. 153–156. ACM (2007)
Wang, Y., Zhang, J., Vassileva, J.: Towards Effective Recommendation of Social Data across Social Networking Sites. In: Dicheva, D., Dochev, D. (eds.) AIMSA 2010. LNCS, vol. 6304, pp. 61–70. Springer, Heidelberg (2010)
Webster, A., Vassileva, J.: The KeepUP Recommender System. In: Proc. of the 2007 ACM Conference on Recommender Systems, RecSys 2007, pp. 173–176. ACM, Minneapolis (2007)
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Nagulendra, S., Vassileva, J. (2013). Providing Awareness, Understanding and Control of Personalized Stream Filtering in a P2P Social Network. In: Antunes, P., Gerosa, M.A., Sylvester, A., Vassileva, J., de Vreede, GJ. (eds) Collaboration and Technology. CRIWG 2013. Lecture Notes in Computer Science, vol 8224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41347-6_6
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DOI: https://doi.org/10.1007/978-3-642-41347-6_6
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