NSF Org:	CNS Division Of Computer and Network Systems
Recipient:	REGENTS OF THE UNIVERSITY OF MICHIGAN
Initial Amendment Date:	May 9, 2019
Latest Amendment Date:	May 9, 2019
Award Number:	1915837
Award Instrument:	Standard Grant
Program Manager:	Sara Kiesler skiesler@nsf.gov  (703)292-8643 CNS  Division Of Computer and Network Systems CSE  Directorate for Computer and Information Science and Engineering
Start Date:	June 1, 2019
End Date:	September 30, 2019 (Estimated)
Total Intended Award Amount:	$150,000.00
Total Awarded Amount to Date:	$150,000.00
Funds Obligated to Date:	FY 2019 = $0.00
History of Investigator:	Brendan Nyhan (Principal Investigator) brendan.j.nyhan@dartmouth.edu
Recipient Sponsored Research Office:	Regents of the University of Michigan - Ann Arbor 1109 GEDDES AVE STE 3300 ANN ARBOR MI  US  48109-1015 (734)763-6438
Sponsor Congressional District:	06
Primary Place of Performance:	Regents of the University of Michigan 3003 South State St. Ann Arbor MI  US  48109-1274
Primary Place of Performance Congressional District:	06
Unique Entity Identifier (UEI):	GNJ7BBP73WE9
Parent UEI:
NSF Program(s):	Secure &Trustworthy Cyberspace
Primary Program Source:	01001920DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s):	025Z, 065Z, 7434, 7916
Program Element Code(s):	806000
Award Agency Code:	4900
Fund Agency Code:	4900
Assistance Listing Number(s):	47.070

ABSTRACT

Social media now play an important role in exposing people to information about a wide range of topics ranging from entertainment to hard news and political debate. What can be seen on these platforms is heavily influenced by algorithms that are designed to select the most engaging and relevant content for each user. By seeking to maximize engagement, these algorithms may inadvertently amplify factually dubious or poor quality information that reinforces users' existing beliefs. In doing so, these algorithms could reduce the diversity of information to which users are exposed. This project will develop new content recommendation algorithms that reduce this risk and improve the quality and diversity of information circulating on social media.

This research will develop an understanding of how coupled cyber-human systems process information in the context of news consumption on social media. This context creates important information-processing vulnerabilities at the social, behavioral, cognitive, and algorithmic levels. Using data from a nationally representative sample of the U.S. population, investigators will measure the association between political attitudes, readership, engagement, and information quality. They will also test the effect of behavioral nudges designed to promote the consumption of diverse information in a browser extension/smartphone app. Finally, the researchers will develop a generic modeling framework to evaluate the effect of these recommendations on audience-slant diversification and to test their robustness against fraudulent (shilling) attacks.

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.

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