IDEAS home Printed from https://ideas.repec.org/a/zbw/iprjir/214006.html
   My bibliography  Save this article

Should we worry about filter bubbles?

Author

Listed:
  • Zuiderveen Borgesius, Frederik J.
  • Trilling, Damian
  • Möller, Judith
  • Bodó, Balázs
  • de Vreese, Claes H.
  • Helberger, Natali

Abstract

Some fear that personalised communication can lead to information cocoons or filter bubbles. For instance, a personalised news website could give more prominence to conservative or liberal media items, based on the (assumed) political interests of the user. As a result, users may encounter only a limited range of political ideas. We synthesise empirical research on the extent and effects of self-selected personalisation, where people actively choose which content they receive, and pre-selected personalisation, where algorithms personalise content for users without any deliberate user choice. We conclude that at present there is little empirical evidence that warrants any worries about filter bubbles.

Suggested Citation

  • Zuiderveen Borgesius, Frederik J. & Trilling, Damian & Möller, Judith & Bodó, Balázs & de Vreese, Claes H. & Helberger, Natali, 2016. "Should we worry about filter bubbles?," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 5(1), pages 1-16.
    • Handle: RePEc:zbw:iprjir:214006
    DOI: 10.14763/2016.1.401
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/214006/1/IntPolRev-2016-1-401.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.14763/2016.1.401?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Robert M. Bond & Christopher J. Fariss & Jason J. Jones & Adam D. I. Kramer & Cameron Marlow & Jaime E. Settle & James H. Fowler, 2012. "A 61-million-person experiment in social influence and political mobilization," Nature, Nature, vol. 489(7415), pages 295-298, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Theodora A. Maniou & Andreas Veglis, 2020. "Employing a Chatbot for News Dissemination during Crisis: Design, Implementation and Evaluation," Future Internet, MDPI, vol. 12(7), pages 1-14, June.
    2. Noskova, Victoriia, 2021. "Voice assistants as gatekeepers for consumption? How information intermediaries shape competition," Ilmenau Economics Discussion Papers 161, Ilmenau University of Technology, Institute of Economics.
    3. Kim, Jungkeun & Kim, Jeong Hyun & Kim, Changju & Park, Jooyoung, 2023. "Decisions with ChatGPT: Reexamining choice overload in ChatGPT recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 75(C).
    4. Guohui Song & Yongbin Wang, 2021. "Mainstream Value Information Push Strategy on Chinese Aggregation News Platform: Evolution, Modelling and Analysis," Sustainability, MDPI, vol. 13(19), pages 1-17, October.
    5. Irion, Kristina & Helberger, Natali, 2017. "Smart TV and the online media sector: User privacy in view of changing market realities," Telecommunications Policy, Elsevier, vol. 41(3), pages 170-184.
    6. Rui Qiao & Cong Liu & Jun Xu, 2024. "Making algorithmic app use a virtuous cycle: Influence of user gratification and fatigue on algorithmic app dependence," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-10, December.
    7. Pelletier, Mark J. & Horky, Alisha Blakeney & Fox, Alexa K., 2021. "Fexit: The effect of political and promotional communication from friends and family on Facebook exiting intentions," Journal of Business Research, Elsevier, vol. 122(C), pages 321-334.
    8. Sætra, Henrik Skaug, 2019. "The tyranny of perceived opinion: Freedom and information in the era of big data," Technology in Society, Elsevier, vol. 59(C).
    9. Germano, Fabrizio & Sobbrio, Francesco, 2020. "Opinion dynamics via search engines (and other algorithmic gatekeepers)," Journal of Public Economics, Elsevier, vol. 187(C).
    10. Kim, Jiwhan & Nam, Changi, 2019. "Analyzing continuance intention of recommendation algorithms," 30th European Regional ITS Conference, Helsinki 2019 205190, International Telecommunications Society (ITS).
    11. Sarah Eskens, 2020. "The personal information sphere: An integral approach to privacy and related information and communication rights," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(9), pages 1116-1128, September.
    12. Leyrer, Katharina, 2018. "Selektion und Bias in traditionellen und Internet-Informationsintermediären: Forschungsstand," Erlangen Contributions to Media Management and Media Economics 10/2018, Friedrich-Alexander University of Erlangen-Nuremberg (FAU), Institute for the Study of the Book, Professorship of E-Publishing and Digital Markets.
    13. Huw C Davies, 2018. "Redefining Filter Bubbles as (Escapable) Socio-Technical Recursion," Sociological Research Online, , vol. 23(3), pages 637-654, September.
    14. Anna Gerbrandy, 2019. "Rethinking Competition Law within the European Economic Constitution," Journal of Common Market Studies, Wiley Blackwell, vol. 57(1), pages 127-142, January.
    15. Copland, Simon, 2020. "Reddit quarantined: Can changing platform affordances reduce hateful material online?," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 9(4), pages 1-26.
    16. Iina Savolainen & Markus Kaakinen & Anu Sirola & Aki Koivula & Heli Hagfors & Izabela Zych & Hye-Jin Paek & Atte Oksanen, 2020. "Online Relationships and Social Media Interaction in Youth Problem Gambling: A Four-Country Study," IJERPH, MDPI, vol. 17(21), pages 1-18, November.
    17. Budzinski, Oliver & Gänßle, Sophia & Lindstädt-Dreusicke, Nadine, 2021. "Data (r)evolution - The economics of algorithmic search and recommender services," Ilmenau Economics Discussion Papers 148, Ilmenau University of Technology, Institute of Economics.
    18. Herzog, Bodo, 2019. "Optimal policy under uncertainty and rational inattention," Research in International Business and Finance, Elsevier, vol. 50(C), pages 444-449.
    19. Annelien Smets & Jorre Vannieuwenhuyze & Pieter Ballon, 2022. "Serendipity in the city: User evaluations of urban recommender systems," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(1), pages 19-30, January.
    20. Thomas E. Powell & Toni G. L. A. van der Meer & Carlos Brenes Peralta, 2019. "Picture Power? The Contribution of Visuals and Text to Partisan Selective Exposure," Media and Communication, Cogitatio Press, vol. 7(3), pages 12-31.
    21. Chulmin Lim & Seongcheol Kim, 2024. "Examining factors influencing the user’s loyalty on algorithmic news recommendation service," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    22. Thiel, Thorsten, 2017. "Digitalisierung als Kontext politischen Handelns. Republikanische Perspektiven auf die digitale Transformation der Gegenwart," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 189-215.
    23. Kris Hartley & Minh Khuong Vu, 2020. "Fighting fake news in the COVID-19 era: policy insights from an equilibrium model," Policy Sciences, Springer;Society of Policy Sciences, vol. 53(4), pages 735-758, December.
    24. König Pascal D., 2020. "Why Digital-Era Political Marketing is Not the Death Knell for Democracy: On the Importance of Placing Political Microtargeting in the Context of Party Competition," Statistics, Politics and Policy, De Gruyter, vol. 11(1), pages 87-110, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Johnson, Nathan & Turnbull, Benjamin & Reisslein, Martin, 2022. "Social media influence, trust, and conflict: An interview based study of leadership perceptions," Technology in Society, Elsevier, vol. 68(C).
    2. Alan Gerber & Mitchell Hoffman & John Morgan & Collin Raymond, 2020. "One in a Million: Field Experiments on Perceived Closeness of the Election and Voter Turnout," American Economic Journal: Applied Economics, American Economic Association, vol. 12(3), pages 287-325, July.
    3. Kenju Kamei & Louis Putterman & Jean-Robert Tyran, 2019. "Civic Engagement as a Second-Order Public Good: The Cooperative Underpinnings of the Accountable State," Discussion Papers 19-10, University of Copenhagen. Department of Economics.
    4. Ruyi Ge & Juan Feng & Bin Gu, 2016. "Borrower’s default and self-disclosure of social media information in P2P lending," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-6, December.
    5. Jiang, Lincheng & Zhao, Xiang & Ge, Bin & Xiao, Weidong & Ruan, Yirun, 2019. "An efficient algorithm for mining a set of influential spreaders in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 58-65.
    6. Kristina Gavin Bigsby & Jeffrey W. Ohlmann & Kang Zhao, 2017. "Online and Off the Field: Predicting School Choice in College Football Recruiting from Social Media Data," Decision Analysis, INFORMS, vol. 14(4), pages 261-273, December.
    7. Yann Algan & Quoc-Anh Do & Nicolò Dalvit & Alexis Le Chapelain & Yves Zenou, 2015. "How Social Networks Shape Our Beliefs: A Natural Experiment among Future French Politicians," Working Papers hal-03459820, HAL.
    8. Mechtenberg, Lydia & Tyran, Jean-Robert, 2019. "Voter motivation and the quality of democratic choice," Games and Economic Behavior, Elsevier, vol. 116(C), pages 241-259.
    9. Daniele Barchiesi & Helen Susannah Moat & Christian Alis & Steven Bishop & Tobias Preis, 2015. "Quantifying International Travel Flows Using Flickr," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-8, July.
    10. repec:spo:wpmain:info:hdl:2441/78vacv4udu92eq3fec89svm9uv is not listed on IDEAS
    11. Carolin V. Zorell, 2020. "Nudges, Norms, or Just Contagion? A Theory on Influences on the Practice of (Non-)Sustainable Behavior," Sustainability, MDPI, vol. 12(24), pages 1-21, December.
    12. Julian Freitag & Anna Kerkhof & Johannes Münster, 2021. "Selective sharing of news items and the political position of news outlets," ECONtribute Discussion Papers Series 056, University of Bonn and University of Cologne, Germany.
    13. Paul Resnick & Eytan Adar & Cliff Lampe, 2015. "What Social Media Data We Are Missing and How to Get It," The ANNALS of the American Academy of Political and Social Science, , vol. 659(1), pages 192-206, May.
    14. Donati, Dante, 2023. "Mobile Internet access and political outcomes: Evidence from South Africa," Journal of Development Economics, Elsevier, vol. 162(C).
    15. Yuho Chung & Yiwei Li & Jianmin Jia, 2021. "Exploring embeddedness, centrality, and social influence on backer behavior: the role of backer networks in crowdfunding," Journal of the Academy of Marketing Science, Springer, vol. 49(5), pages 925-946, September.
    16. David Ehrlich & Nora Szech, 2022. "How To Start a Grassroots Movement," Papers 2209.01345, arXiv.org.
    17. Liberini, Federica & Redoano, Michela & Russo, Antonio & Cuevas, Angel & Cuevas, Ruben, 2018. "Politics in the Facebook Era Evidence from the 2016 US Presidential Elections," CAGE Online Working Paper Series 389, Competitive Advantage in the Global Economy (CAGE).
    18. Mohammad Abouei Mehrizi & Federico Corò & Emilio Cruciani & Gianlorenzo D’Angelo, 2022. "Election control through social influence with voters’ uncertainty," Journal of Combinatorial Optimization, Springer, vol. 44(1), pages 635-669, August.
    19. Sun, Jiachen & Feng, Ling & Du, Mingwei & Ma, Xiao & Fan, Zhengping & Gloor, Peter & Hu, Yanqing, 2021. "Ultra-efficient information detection on large-scale online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    20. repec:hal:spmain:info:hdl:2441/78vacv4udu92eq3fec89svm9uv is not listed on IDEAS
    21. Liu, Jian-Guo & Li, Ren-De & Guo, Qiang & Zhang, Yi-Cheng, 2018. "Collective iteration behavior for online social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 490-497.
    22. Sabrina Stöckli & Doris Hofer, 2020. "Susceptibility to social influence predicts behavior on Facebook," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:iprjir:214006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://policyreview.info/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.