Characterization of Misinformation Dynamics in COVID-19 related health information in online social media

  • Funded by National Institutes of Health (NIH)
  • Total publications:0 publications

Grant number: unknown

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $63,106
  • Funder

    National Institutes of Health (NIH)
  • Principle Investigator

    Pending
  • Research Location

    United States of America, Americas
  • Lead Research Institution

    The University of Texas Health Science Center at Houston
  • Research Category

    Policies for public health, disease control & community resilience

  • Research Subcategory

    Communication

  • Special Interest Tags

    Gender

  • Study Subject

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Abstract:Social media has become predominant as a source of information for many health care consumers. Howeverfalse and misleading information are a pervasive problem in this context. Specifically, during CVID-19 pandemic,misinformation has been a significant public health challenge, impeding the effectiveness of public healthawareness campaigns and resulting in suboptimal responsiveness to the communication of legitimate risk-related information. In the proposed research, we will apply our "Pragmatics to Reveal Intent in Social Media(PRISM) framework to facilitate automated detection of intent and belief attributes underlying COVID-19 relatedmisinformation. The PRISM framework aims to incorporate and integrate communication intent, semantics andstructure of online communication to study social processes and cognitive factors underlying misinformationcomprehension. Such analysis forms the foundational step towards characterization of misinformation seedingand perception in digital social settings, ultimately allowing us to develop scalable and reliable computationalinfrastructure that can help formulate resilient and effective dissemination approaches to negotiatemisinformation spread, easing public health burden and informing policy regulations as needed.