RAPID: COVID-19 and Perceptions of Electoral Integrity

  • Funded by National Science Foundation (NSF)
  • Total publications:0 publications

Grant number: 2103262

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Key facts

  • Disease

    COVID-19
  • Start & end year

    2020
    2021
  • Known Financial Commitments (USD)

    $180,448
  • Funder

    National Science Foundation (NSF)
  • Principal Investigator

    Brian Fogarty
  • Research Location

    United States of America
  • Lead Research Institution

    University of Notre Dame
  • Research Priority Alignment

    N/A
  • Research Category

    Policies for public health, disease control & community resilience

  • Research Subcategory

    Communication

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Unspecified

  • Vulnerable Population

    Unspecified

  • Occupations of Interest

    Unspecified

Abstract

Concerns regarding election fraud and election manipulation associated with the substantial expansion of voting by mail in the 2020 elections due to COVID-19 may threaten citizens' trust in American election outcomes and the electoral system. This study provides insights into public beliefs about voter fraud related to mail-in voting and the effectiveness of new information provided through social media and media outlets. The study also assesses how people's beliefs and experiences with COVID-19 are related to their perceptions of electoral integrity.

The study collects behavioral and survey data from Americans in a multi-wave nationally representative survey. The panel survey design compares the accuracy of citizens' factual beliefs about the prevalence of voter fraud between late summer 2020 and two survey waves conducted immediately following the election in November 2020 and just prior to the presidential inauguration in January 2021. The second wave includes an experiment evaluating the effectiveness of corrective information from the media. The study assesses the information people encounter online about the election by analyzing behavioral data from respondents using human-coded and machine learning approaches.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.