article

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

Authors:

Sayooran Nagulendra,

Julita VassilevaAuthors Info & Claims

Information Systems Frontiers, Volume 18, Issue 1

Pages 145 - 158

https://doi.org/10.1007/s10796-015-9577-y

Published: 01 February 2016 Publication History

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.

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cover image Information Systems Frontiers

Information Systems Frontiers Volume 18, Issue 1

February 2016

229 pages

ISSN:1387-3326

Issue’s Table of Contents

Copyright © Copyright © 2016 Springer Science+Business Media New York.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 February 2016

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Cited By

Neifer TBossauer PPakusch CBoehm LLawo D(2024)Trust-Building in Peer-to-Peer Carsharing: Design Case Study for Algorithm-Based Reputation SystemsComputer Supported Cooperative Work10.1007/s10606-022-09461-433:2(137-171)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10606-022-09461-4
Guesmi MSiepmann CChatti MJoarder SAin QAlatrash R(2023)Validation of the EDUSS Framework for Self-Actualization Based on Transparent User Models: A Qualitative StudyAdjunct Proceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3563359.3597379(229-238)Online publication date: 26-Jun-2023
https://dl.acm.org/doi/10.1145/3563359.3597379
Brunotte WSpecht AChazette LSchneider K(2023)Privacy explanations – A means to end-user trustJournal of Systems and Software10.1016/j.jss.2022.111545195:COnline publication date: 8-Feb-2023
https://dl.acm.org/doi/10.1016/j.jss.2022.111545
Bawack RBonhoure E(2023)Influencer is the New Recommender: insights for Theorising Social Recommender SystemsInformation Systems Frontiers10.1007/s10796-022-10262-925:1(183-197)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s10796-022-10262-9
Kunkel JSchwenger CZiegler J(2020)NewsViz: Depicting and Controlling Preference Profiles Using Interactive Treemaps in News Recommender SystemsProceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3340631.3394869(126-135)Online publication date: 7-Jul-2020
https://dl.acm.org/doi/10.1145/3340631.3394869
Burbach LHalbach PZiefle MCalero�Valdez A(2019)Bubble Trouble: Strategies Against Filter Bubbles in Online Social NetworksDigital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications10.1007/978-3-030-22219-2_33(441-456)Online publication date: 26-Jul-2019
https://dl.acm.org/doi/10.1007/978-3-030-22219-2_33
Rader ECotter KCho JMandryk RHancock MPerry MCox A(2018)Explanations as Mechanisms for Supporting Algorithmic TransparencyProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173677(1-13)Online publication date: 21-Apr-2018
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Reviglio U(2017)Serendipity by Design? How to Turn from Diversity Exposure to Diversity Experience to Face Filter Bubbles in Social MediaInternet Science10.1007/978-3-319-70284-1_22(281-300)Online publication date: 22-Nov-2017
https://dl.acm.org/doi/10.1007/978-3-319-70284-1_22

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