Facebook’s News Feed Algorithm and the 2020 US Election

Abstract

Facebook plays a substantial role in shaping exposure to news and information, a role that is particularly significant during democratic elections. This article aims to characterize Facebook’s contribution to news exposure during the 2020 US election. Using internet browsing data from 1.3 million desktop panelists in the United States, we analyze audience flows from Facebook to news and information publishers ( N = 3,247 websites) labeled according to quality (high or low) and geography (non-local or local). We find that Facebook ranked as the fourth largest external referral source to publishers. Our analysis further shows that, among the panel of 1.3 million US adults visiting these publishers via desktop computers, Facebook disproportionately amplified low-quality publishers and extended their audience reach during the study period. We also find evidence that Facebook’s intervention to improve “news ecosystem quality” by tweaking their News Feed algorithm was associated with fewer daily visits to low-quality publishers, but seemed to have a similar effect on other publishers and, according to Facebook-provided data, had little effect on month-over-month traffic. We discuss limitations, open questions about platform impact, and directions for future work.