NSF Org:	SES Division of Social and Economic Sciences
Recipient:	UNIVERSITY OF OKLAHOMA
Initial Amendment Date:	March 30, 2020
Latest Amendment Date:	March 30, 2020
Award Number:	2026763
Award Instrument:	Standard Grant
Program Manager:	Robert O'Connor roconnor@nsf.gov  (703)292-7263 SES  Division of Social and Economic Sciences SBE  Directorate for Social, Behavioral and Economic Sciences
Start Date:	April 1, 2020
End Date:	March 31, 2021 (Estimated)
Total Intended Award Amount:	$200,000.00
Total Awarded Amount to Date:	$200,000.00
Funds Obligated to Date:	FY 2020 = $200,000.00
History of Investigator:	Hank Jenkins-Smith (Principal Investigator) hjsmith@ou.edu Scott Robinson (Co-Principal Investigator) Carol Silva (Co-Principal Investigator) Joseph Ripberger (Co-Principal Investigator) Kuhika Gupta (Co-Principal Investigator)
Recipient Sponsored Research Office:	University of Oklahoma Norman Campus 660 PARRINGTON OVAL RM 301 NORMAN OK  US  73019-3003 (405)325-4757
Sponsor Congressional District:	04
Primary Place of Performance:	University of Oklahoma Norman Campus OK  US  73019-9705
Primary Place of Performance Congressional District:	04
Unique Entity Identifier (UEI):	EVTSTTLCEWS5
Parent UEI:
NSF Program(s):	Decision, Risk & Mgmt Sci, Secure &Trustworthy Cyberspace
Primary Program Source:	01002021DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	025Z, 065Z, 096Z, 7434, 7914, 8060, 9150, 9178, 9179
Program Element Code(s):	132100, 806000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.075

ABSTRACT

The current spread of, and alarm about, the COVID-19 virus provides a unique and ephemeral opportunity to obtain meaningful time-series survey data on public beliefs, attitudes, behaviors, and the receipt of information of various kinds about the disease and its effects on taking protective action. The National Institute for Risk and Resilience (NIRR) utilizes its on-going Twitter data collection associated with coronavirus (collected since January 2020), and undertakes a series of monthly nation-wide surveys on public views to test the broader publics? receipt of, trust in, and use of information about the virus posted on social media. The surveys will include questions about protective action behavior, trust in key actors, perceptions of risk associated with the outbreak, and perceptions of information accuracy/inaccuracy. The complementary survey and social media data streams will allow tracking the spread and penetration of information over time and as the disease spreads in order to match various narratives as they emerge on social media along with beliefs measured in the contemporaneous survey data. The time sensitive data will permit testing of hypotheses about the dynamic relationships between the spread of information in social media, broader public beliefs and behaviors, and effects on protective behaviors that may influence the spread of contagious diseases.

The goal of this study is to measure and track the influence of information about the COVID-19 pandemic on Twitter among members of the broader US public. The study integrates two complementary streams of data to systematically examine the impact of information bubbles and various forms of information on protection motivation and actions in response to the COVID-19 outbreak in the US. First, since January 2020 ,the research team has collected all messages on Twitter that relate to COVID-19, by establishing a connection with the Twitter streaming API. The team obtains all posts and metadata that include any of the following key words: coronavirus, COVID-19, SARS-CoV-2, #coronavirus, #2019_nCov, and #COVID-19. From January 27 to Feb 24, the team collected more than 31 million different messages about the virus. The Twitter posts provide a continuous flow of data about the evolution of information networks and the promulgation and spread of information, but they do not provide information on the extent to which these factors are affecting protective motivations in the broader public and shaping the perceptions that drive them (such as trust in perceived risk). Second, the team collects online rolling nationwide surveys of the broader public?s understanding of COVID-19, with special attention to beliefs about the information that appears on Twitter, over the span of the next year. There are 10 nationwide surveys in all, one each month (time-series cross-sections), with collections timed to obtain 250 responses each week to increase the ability to quickly identify changes in beliefs, perceptions and associated protective behaviors. The surveys are designed to allow pairing the changing pattern of information of various sorts on social media with the receipt and belief of that information among the broader public. The experiments draw from the rise and spread of different kinds of information on Twitter.

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.

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

This project focused on the connections between the COVID-19 information ecosystem, core beliefs and risk perceptions, exposure and vulnerability to misinformation, and protective behaviors in response to the pandemic. Figure 1 summarizes the framework we used to study these connections.

We used millions of messages on Twitter to systematically study and characterize the information ecosystem. To measure public beliefs, exposure and vulnerability to misinformation, trust, appraisals, and behaviors, we conducted monthly nationwide surveys from March 2020 through March 2021. Overall, 11,081 survey responses were collected. Consistent with the framework in Figure 1, we were able to identify a number of important findings that were persistent throughout the study period: (1) there was extreme polarization in exposure and vulnerability to misinformation?Republicans and Democrats saw and believed very different information about COVID-19; (2) people who routinely saw and believed misinformation were less likely to trust public health experts than people who were able to ignore or see through misinformation; (3) people with low levels of trust in public health experts had low threat appraisals in comparison to people with high levels of trust; and (4) people with low threat appraisals were much less likely to engage in protective behaviors and support protective policies that limited the spread of COVID-19 than people with high threat appraisals. These findings are significant because they help to document the persistent and long-lasting negative impacts of misinformation on public responses to emergent risks. In addition to short-term confusion, inaccurate beliefs, and dangerous behaviors, our findings show that misinformation can perpetuate a long-term cycle of mistrust that makes it difficult to correct future misinformation and, more generally, to disseminate accurate information about risk.

Thus far, this work has resulted in a published paper in Public Administration Review and a book under review at Cambridge University Press.

 

 

 

 

Last Modified: 04/01/2021
Modified by: Hank C Jenkins-Smith

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