Article

Bubble Trouble: Strategies Against Filter Bubbles in Online Social Networks

Authors:

Patrick Halbach,

Martina Ziefle,

André Calero ValdezAuthors Info & Claims

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications: 10th International Conference, DHM 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part II

Pages 441 - 456

https://doi.org/10.1007/978-3-030-22219-2_33

Published: 26 July 2019 Publication History

Abstract

In the recent past, some electoral decisions have gone against the pre-election expectations, what led to greater emphasis on social networking in the creation of filter bubbles. In this article, we examine whether Facebook usage motives, personality traits of Facebook users, and awareness of the filter bubble phenomenon influence whether and how Facebook users take action against filter bubbles. To answer these questions we conducted an online survey with 149 participants in Germany. While we found out that in our sample, the motives for using Facebook and the awareness of the filter bubble have an influence on whether a person consciously takes action against the filter bubble, we found no influence of personality traits. The results show that Facebook users know for the most part that filter bubbles exist, but still do little about them. Therefore it can be concluded that in today’s digital age, it is important not only to inform users about the existence of filter bubbles, but also about various possible strategies for dealing with them.

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cover image Guide Proceedings

Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management. Healthcare Applications: 10th International Conference, DHM 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part II

Jul 2019

549 pages

ISBN:978-3-030-22218-5

DOI:10.1007/978-3-030-22219-2

Editor:
Vincent G. Duffy
Purdue University, West Lafayette, IN, USA

� Springer Nature Switzerland AG 2019.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 26 July 2019

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Cited By

Cai WFeng FWang QYang TLiu ZXu CChua TLauw HSi LTerzi ETsaparas P(2023)A Causal View for Item-level Effect of Recommendation on User PreferenceProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570461(240-248)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570461
Theophilou ELomonaco FDonabauer GOgnibene DS�nchez-Reina RHern�ndez-Leo D(2023)AI and�Narrative Scripts to�Educate Adolescents About Social Media Algorithms: Insights About AI Overdependence, Trust and�AwarenessResponsive and Sustainable Educational Futures10.1007/978-3-031-42682-7_28(415-429)Online publication date: 4-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-42682-7_28
Bhuiyan MBautista Isaza CMitra TLee S(2022)OtherTube: Facilitating Content Discovery and Reflection by Exchanging YouTube Recommendations with StrangersProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502028(1-17)Online publication date: 29-Apr-2022
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https://dl.acm.org/doi/10.1007/978-3-030-49907-5_6

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