First findings from U.S. 2020 Facebook and Instagram election study

Research by Annenberg School for Communication professor Sandra González-Bailón and colleagues reveals the influence of Facebook’s algorithms on political news exposure.

Academics from U.S. colleges and universities, including Annenberg School for Communication professor Sandra González-Bailón, working in collaboration with researchers at Meta, have published findings from the first set of four papers as part of the most comprehensive research project to date examining the role of social media in American democracy.

Hands holding a smartphone open to Facebook in front of a laptop open to Facebook.
Image: Adobe stock/Thaspol

The papers, which focus primarily on how critical aspects of the algorithms that determine what people see in their feeds affect what people see and believe, are published in Science and Nature.

González-Bailón, the Carolyn Marvin Professor of Communication and Director of the Center for Information Networks and Democracy, is the lead author of the Science paper entitled “Asymmetric Ideological Segregation in Exposure to Political News on Facebook.” She is also a co-author on the remaining four papers.

The academic team proposed and selected specific research questions and study designs with the explicit agreement that the only reasons Meta could reject such designs would be for legal, privacy, or logistical (i.e., infeasibility) reasons. Meta could not restrict or censor findings, and the academic lead authors had final say over writing and research decisions.

The team found that algorithms are extremely influential in terms of what people see and in shaping the on-platform experiences. There is significant ideological segregation in political news exposure. Three experiments conducted with consenting participants run during the 2020 election period suggest that although algorithm adjustments significantly change what people see and their level of engagement on the platforms, the three-month experimental modifications did not notably affect political attitudes.

Asymmetric Ideological Segregation in Exposure to Political News on Facebook,” co-led by González-Bailón, respectively, analyzed on-platform exposure to political news URLs during the U.S. 2020 election and compared the inventory of all the political news links U.S. users could have seen in their feeds with the information they saw and the information with which they engaged.

“This begins to answer questions about the complex interaction between social and algorithmic choices in the curation of political news and how that played out on Facebook in the 2020 election,” says González-Bailón.

Read more at Annenberg School for Communication.