IDEAS home Printed from https://ideas.repec.org/a/spr/comaot/v25y2019i1d10.1007_s10588-018-09280-3.html
   My bibliography  Save this article

FakeNewsTracker: a tool for fake news collection, detection, and visualization

Author

Listed:
  • Kai Shu

    (Arizona State University)

  • Deepak Mahudeswaran

    (Arizona State University)

  • Huan Liu

    (Arizona State University)

Abstract

Nowadays social media is widely used as the source of information because of its low cost, easy to access nature. However, consuming news from social media is a double-edged sword because of the wide propagation of fake news, i.e., news with intentionally false information. Fake news is a serious problem because it has negative impacts on individuals as well as society large. In the social media the information is spread fast and hence detection mechanism should be able to predict news fast enough to stop the dissemination of fake news. Therefore, detecting fake news on social media is an extremely important and also a technically challenging problem. In this paper, we present FakeNewsTracker, a system for fake news understanding and detection. As we will show, FakeNewsTracker can automatically collect data for news pieces and social context, which benefits further research of understanding and predicting fake news with effective visualization techniques.

Suggested Citation

  • Kai Shu & Deepak Mahudeswaran & Huan Liu, 2019. "FakeNewsTracker: a tool for fake news collection, detection, and visualization," Computational and Mathematical Organization Theory, Springer, vol. 25(1), pages 60-71, March.
    • Handle: RePEc:spr:comaot:v:25:y:2019:i:1:d:10.1007_s10588-018-09280-3
    DOI: 10.1007/s10588-018-09280-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10588-018-09280-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10588-018-09280-3?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.

    Citations

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


    Cited by:

    1. Kitti Nagy & Jozef Kapusta, 2021. "Improving fake news classification using dependency grammar," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.

    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:spr:comaot:v:25:y:2019:i:1:d:10.1007_s10588-018-09280-3. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.