IDEAS home Printed from https://ideas.repec.org/a/eee/juecon/v141y2024ics0094119023000839.html
   My bibliography  Save this article

Spatial and historical drivers of fake news diffusion: Evidence from anti-Muslim discrimination in India

Author

Listed:
  • Abraham, Samira S.
  • Lanzara, Gianandrea
  • Lazzaroni, Sara
  • Masella, Paolo
  • Squicciarini, Mara P.

Abstract

What drives the propagation of discriminatory fake news? To answer this question, this paper focuses on India at the onset of the COVID-19 pandemic: on March 30, a Muslim convention (the Tablighi Jamaat) in New Delhi became publicly recognized as a COVID hotspot. Using Twitter data, we build a comprehensive novel dataset of georeferenced tweets to identify anti-Muslim fake news. First, we document that fake news about Muslims intentionally spreading the virus spiked after March 30. Then, we investigate the geographical and historical determinants of the spread of fake news in a difference-in-difference setting. We find that the diffusion of anti-Muslim false stories was more pronounced (i) in districts closer to New Delhi, suggesting that fake news spread spatially; and (ii) in districts exposed to historical attacks by Muslim groups, suggesting that the propensity to disseminate fake news has deep-rooted historical origins.

Suggested Citation

  • Abraham, Samira S. & Lanzara, Gianandrea & Lazzaroni, Sara & Masella, Paolo & Squicciarini, Mara P., 2024. "Spatial and historical drivers of fake news diffusion: Evidence from anti-Muslim discrimination in India," Journal of Urban Economics, Elsevier, vol. 141(C).
    • Handle: RePEc:eee:juecon:v:141:y:2024:i:c:s0094119023000839
    DOI: 10.1016/j.jue.2023.103613
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0094119023000839
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.jue.2023.103613?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.

    More about this item

    Keywords

    Discrimination; Fake news; Religion; Covid; India;
    All these keywords.

    JEL classification:

    • J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
    • Z12 - Other Special Topics - - Cultural Economics - - - Religion

    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:eee:juecon:v:141:y:2024:i:c:s0094119023000839. 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/inca/622905 .

    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.