
Administratively Terminated Award | |
NSF Org: |
SES Division of Social and Economic Sciences |
Recipient: |
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Initial Amendment Date: | June 14, 2022 |
Latest Amendment Date: | May 16, 2025 |
Award Number: | 2230083 |
Award Instrument: | Standard Grant |
Program Manager: |
Sara Kiesler
skiesler@nsf.gov (703)292-8643 SES Division of Social and Economic Sciences SBE Directorate for Social, Behavioral and Economic Sciences |
Start Date: | July 1, 2022 |
End Date: | April 18, 2025 (Estimated) |
Total Intended Award Amount: | $199,786.00 |
Total Awarded Amount to Date: | $199,786.00 |
Funds Obligated to Date: |
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History of Investigator: |
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Recipient Sponsored Research Office: |
1500 HORNING RD KENT OH US 44242-0001 (330)672-2070 |
Sponsor Congressional District: |
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Primary Place of Performance: |
1500 HORNING RD KENT OH US 44242-0001 |
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): | Secure &Trustworthy Cyberspace |
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
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EAGER: SaTC: CORE: Small: Shifts in misinformation topics on social media: manipulators masquerading as humans
The spread of misinformation on social media can result in major consequences to the health, wellbeing, and stability of the general public. A wide range of topics are vulnerable to misinformation, varying from medical misinformation to political misinformation. Accounts that spread misinformation can be broadly classified into two categories: (1) those who do so unintentionally (i.e., individuals who believe in the misinformation that they spread) and (2) those who do so with the aim of being deliberately deceptive (i.e., agents of disinformation ?masquerading? as humans). Those in the former category typically spread misinformation on a constrained number of topics (i.e., either medical or political, but not both), focusing on what they care about as individuals. However, agents of disinformation may be incentivized by malicious third-party actors to spread misinformation across an unconstrained variety of topics, with the objective of prompting widespread instability among the general public. This project analyzes misinformation spread on social media to distinguish third-party-incentivized agents of disinformation from other, more benign accounts.
To achieve this goal, the team will examine data from Twitter to identify accounts that switched rapidly between spreading medical misinformation to spreading political misinformation during the first half of 2022. A machine learning framework will be designed to learn from linguistic features that are unique to this subset of accounts, which will then be used to develop a classification tool to label accounts across the broader Twittersphere (i.e., pre-2022 and post-2022) as ?potential agents of disinformation?. The team will also characterize what fraction of misinformation spread during the first half of 2022 was attributable to such third-party-incentivized agents. All algorithms developed over the course of the project will be shared openly with the broader scientific community to facilitate efforts towards countering disinformation on social media.
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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