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.
EAGER: SaTC: Early-Stage Interdisciplinary Collaboration: Collaborative: Advances in Socio-Algorithmic Information Diversity