| NSF Org: |
SMA SBE Office of Multidisciplinary Activities |
| Recipient: |
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| Initial Amendment Date: | August 13, 2020 |
| Latest Amendment Date: | July 25, 2022 |
| Award Number: | 2031768 |
| Award Instrument: | Standard Grant |
| Program Manager: |
Thomas S. Woodson
tswoodso@nsf.gov (703)292-5150 SMA SBE Office of Multidisciplinary Activities SBE Directorate for Social, Behavioral and Economic Sciences |
| Start Date: | August 15, 2020 |
| End Date: | July 31, 2023 (Estimated) |
| Total Intended Award Amount: | $149,858.00 |
| Total Awarded Amount to Date: | $149,858.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
506 S WRIGHT ST URBANA IL US 61801-3620 (217)333-2187 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
Suite A Champaign IL US 61820-7406 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | Science of Science |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.075 |
ABSTRACT
This project examines the ways that misinformation regarding COVID-19 disseminates through social media and news outlets. The spread of misinformation regarding COVID-19 is part of a larger interest in the communication of accurate and timely scientific information to the public. This project assembles data on English-language news feeds devoted to COVID-19 as well as Facebook, Reddit, twitter, and other social media posts related to COVID-19 to assess the spread of scientific misinformation through social media and the internet with the goal of aiding officials and science advisors in limiting the spread of misinformation as the pandemic unfolds. The ultimate goal is to use machine learning and network analysis tools to provide insight into the locations where misinformation originates and understanding of the mechanisms through which this misinformation spreads.
Our project uses the pre-existing infrastructure of the Cline Center and the social media macroscope at the University of Illinois Urbana-Champaign to track the spread of dubious and potentially harmful information on the origins, spread, and treatments for COVID-19. The researchers will use prevailing insights from the Centers for Disease Control to assess what is known currently about COVID-19 and then develop a machine-learning algorithm to detect information on the web and on social media that will be labelled as potentially dubious. The extraction of claims, people, places, and things from these posts will be used to track locations, people, and organizational affiliations of dubious COVID-19 information. The resulting data will be subjected to network analysis techniques that search for structural holes and weak ties through which misinformation spreads with the goal of providing scientists and policy makers with potential information mitigation strategies. The textual and network modelling this project provides will be immediately available through a University of Illinois COVID-19 website, disseminated through press releases and outreach to policy-making community, and integrated into scientific publications that will be published in open-source formats. The long-term implications of our analysis and the ability to identify hot spots and fault-lines in public communication networks and social media sites will be useful for fighting future pandemics as well as other topics that are vital to the health of the Nation.
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.
PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH
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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.
The spread of social media misinformation during the COVID pandemic complicated the response of healthcare providers and public health administrators in the United States and around the world. This project developed a mechanism for evaluating the effectiveness of one small intervention, misinformation labelling on social media sites, for reducing the spread of COVID-19 misinformation. The researchers used AI/Machine learning techniques to track the spread of communications on social media regarding COVID-19. Our results suggest that social media labelling was reasonably effective at reducing the spread of misinformation regarding COVID-19. The overall results suggest that very small nudges interfere with the normal processes of reading and sharing social media information on the dominant platforms, a result that has been confirmed for other forms of social media information.
The investigators have continued their research into the more general realm of health misinformation and are devising ways for public health officials to be aware of social media communications regarding various diseases and treatments. The ability of healthcare providers and public health officials to know what the general public is saying about different issues relating to health and well-being not only prepares them for what they might encouter, but also increases the chances that they can devise more effective responses to future health emergencies.
Last Modified: 12/07/2023
Modified by: Kevin T Leicht
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