Award Abstract # 2230083
EAGER: SaTC: Shifts in Misinformation Topics on Social Media: Manipulators Masquerading as Humans

Administratively Terminated Award
NSF Org: SES
Division of Social and Economic Sciences
Recipient: KENT STATE UNIVERSITY
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: FY 2022 = $199,786.00
History of Investigator:
  • Helen Piontkivska (Principal Investigator)
    opiontki@kent.edu
  • Maimuna Majumder (Co-Principal Investigator)
Recipient Sponsored Research Office: Kent State University
1500 HORNING RD
KENT
OH  US  44242-0001
(330)672-2070
Sponsor Congressional District: 14
Primary Place of Performance: Kent State University
1500 HORNING RD
KENT
OH  US  44242-0001
Primary Place of Performance
Congressional District:
14
Unique Entity Identifier (UEI): KXNVA7JCC5K6
Parent UEI:
NSF Program(s): Secure &Trustworthy Cyberspace
Primary Program Source: 01002223DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 025Z, 065Z, 7434, 7916, 9102, 9178
Program Element Code(s): 806000
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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Ramjee, Divya and Pollack, Catherine C and Charpignon, Marie-Laure and Gupta, Shagun and Rivera, Jessica Malaty and El_Hayek, Ghinwa and Dunn, Adam G and Desai, Angel N and Majumder, Maimuna S "Evolving Face Mask Guidance During a Pandemic and Potential Harm to Public Perception: Infodemiology Study of Sentiment and Emotion on Twitter" Journal of Medical Internet Research , v.25 , 2023 https://doi.org/10.2196/40706 Citation Details

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