IDEAS home Printed from https://ideas.repec.org/p/ajk/ajkdps/372.html
   My bibliography  Save this paper

News Customization with AI

Author

Listed:
  • Felix Chopra

    (Frankfurt School of Finance & Management)

  • Ingar Haaland

    (NHH Norwegian School of Economics)

  • Fabian Roeben

    (University of Cologne)

  • Christopher Roth

    (University of Cologne, NHH Norwegian School of Economics, Max Planck Institute for Research on Collective Goods, IZA, CEPR)

  • Vanessa Sticher

    (Massachusetts Institute of Technology)

Abstract

News outlets compete for engagement rather than reader satisfaction, leading to persistent mismatches between consumer demand and the supply of news. We test whether offering people the opportunity to customize the news can address this mismatch by unbundling presentation from coverage. In our AI-powered news app, users can customize article characteristics, such as the complexity of the writing or the extent of opinion, while holding the underlying news event constant. Using rich news consumption data from large-scale field experiments, we uncover substantial heterogeneity in news preferences. While a significant fraction of users demand politically aligned news, the vast majority of users display a high and persistent demand for less opinionated and more fact-driven news. Customization also leads to a better match between the news consumed and stated preferences, increasing news satisfaction.

Suggested Citation

  • Felix Chopra & Ingar Haaland & Fabian Roeben & Christopher Roth & Vanessa Sticher, 2025. "News Customization with AI," ECONtribute Discussion Papers Series 372, University of Bonn and University of Cologne, Germany.
  • Handle: RePEc:ajk:ajkdps:372
    as

    Download full text from publisher

    File URL: https://www.econtribute.de/RePEc/ajk/ajkdps/ECONtribute_372_2025.pdf
    File Function: First version, 2025
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • P00 - Political Economy and Comparative Economic Systems - - General - - - General

    Statistics

    Access and download statistics

    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:ajk:ajkdps:372. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: ECONtribute Office (email available below). General contact details of provider: https://www.econtribute.de .

    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.