IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-030-75166-1_33.html
   My bibliography  Save this book chapter

Exploring Social Media Misinformation in the COVID-19 Pandemic Using a Convolutional Neural Network

In: AI and Analytics for Public Health

Author

Listed:
  • Alexander J. Little

    (Ingram School of Engineering, Texas State University)

  • Zhijie Sasha Dong

    (Ingram School of Engineering, Texas State University)

  • Andrew H. Little

    (Network Surveillance Engineering, Consolidated Communications)

  • Guo Qiu

    (Ingram School of Engineering, Texas State University
    College of Engineering and Applied Sciences, Nanjing University)

Abstract

Misinformation is rampant in the modern information age and understanding how social media misinformation diffuses can provide vital insight on how to combat it. With social media becoming a major information source, it is increasingly important to address this concern. Social media misinformation has negatively impacted healthcare response in the past and may have played a major role in how to respond to COVID-19. Understanding how misinformation diffuses through online social networks can provide help healthcare and government entities information on how to mitigate the associated negative impact. This paper proposes a data set as criterion for identifying pandemic specific misinformation and develops a Convolution Neural Network model and. A case study is then conducted to illustrate how diffusion can be explored using labelled misinformation. The work shows a decrease of COVID-19 misinformation over time and a pattern that does not depend on regional geographic location.

Suggested Citation

  • Alexander J. Little & Zhijie Sasha Dong & Andrew H. Little & Guo Qiu, 2022. "Exploring Social Media Misinformation in the COVID-19 Pandemic Using a Convolutional Neural Network," Springer Proceedings in Business and Economics, in: Hui Yang & Robin Qiu & Weiwei Chen (ed.), AI and Analytics for Public Health, pages 443-452, Springer.
    • Handle: RePEc:spr:prbchp:978-3-030-75166-1_33
    DOI: 10.1007/978-3-030-75166-1_33
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:prbchp:978-3-030-75166-1_33. 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.