IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v232y2023ics0165176523003427.html
   My bibliography  Save this article

Generative AI and deceptive news consumption

Author

Listed:
  • Sandrini, Luca
  • Somogyi, Robert

Abstract

In this paper, we analyze the effects of advancements in generative Artificial Intelligence (GenAI) on the news media market. We model a representative consumer who allocates their time between reading news and deceptive articles. We find that GenAI may induce consumers to inefficiently reallocate their time and increase the consumption of the lower value good, i.e. deceptive content (clickbait articles or fake news). Therefore, early-stage GenAI distorts the incentives of consumers and reduces their welfare. After GenAI technology reaches a certain threshold, however, consumers start benefiting from its advancements. Finally, we find that the negative effects of early-stage GenAI are exacerbated as they induce a lower level of investment in news production.

Suggested Citation

  • Sandrini, Luca & Somogyi, Robert, 2023. "Generative AI and deceptive news consumption," Economics Letters, Elsevier, vol. 232(C).
  • Handle: RePEc:eee:ecolet:v:232:y:2023:i:c:s0165176523003427
    DOI: 10.1016/j.econlet.2023.111317
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176523003427
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.econlet.2023.111317?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:ecolet:v:232:y:2023:i:c:s0165176523003427. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolet .

    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.