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Providing awareness, explanation and control of personalized filtering in a social networking site

Author

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  • Sayooran Nagulendra

    (University of Saskatchewan)

  • Julita Vassileva

    (University of Saskatchewan)

Abstract

Social networking sites (SNSs) have applied personalized filtering to deal with overwhelmingly irrelevant social data. However, due to the focus of accuracy, the personalized filtering often leads to “the filter bubble” problem where the users can only receive information that matches their pre-stated preferences but fail to be exposed to new topics. Moreover, these SNSs are black boxes, providing no transparency for the user about how the filtering mechanism decides what is to be shown in the activity stream. As a result, the user’s usage experience and trust in the system can decline. This paper presents an interactive method to visualize the personalized filtering in SNSs. The proposed visualization helps to create awareness, explanation, and control of personalized filtering to alleviate the “filter bubble” problem and increase the users’ trust in the system. Three user evaluations are presented. The results show that users have a good understanding about the filter bubble visualization, and the visualization can increase users’ awareness of the filter bubble, understandability of the filtering mechanism and to a feeling of control over the data stream they are seeing. The intuitiveness of the design is overall good, but a context sensitive help is also preferred. Moreover, the visualization can provide users with better usage experience and increase users’ trust in the system.

Suggested Citation

  • Sayooran Nagulendra & Julita Vassileva, 2016. "Providing awareness, explanation and control of personalized filtering in a social networking site," Information Systems Frontiers, Springer, vol. 18(1), pages 145-158, February.
    • Handle: RePEc:spr:infosf:v:18:y:2016:i:1:d:10.1007_s10796-015-9577-y
    DOI: 10.1007/s10796-015-9577-y
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    References listed on IDEAS

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    1. Rajagopal, 2014. "The Human Factors," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 9, pages 225-249, Palgrave Macmillan.
    2. Matt, C. & Benlian, Alexander & Hess, Thomas & Weiß, Christian, 2014. "Escaping from the Filter Bubble? The Effects of Novelty and Serendipity on Users’ Evaluations of Online Recommendations," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 66193, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    3. repec:cup:judgdm:v:5:y:2010:i:5:p:411-419 is not listed on IDEAS
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    Cited by:

    1. Ransome Epie Bawack & Emilie Bonhoure, 2023. "Influencer is the New Recommender: insights for Theorising Social Recommender Systems," Information Systems Frontiers, Springer, vol. 25(1), pages 183-197, February.
    2. Gert-Jan Vreede & Pedro Antunes & Julita Vassileva & Marco Aurélio Gerosa & Kewen Wu, 2016. "Collaboration technology in teams and organizations: Introduction to the special issue," Information Systems Frontiers, Springer, vol. 18(1), pages 1-6, February.

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