The diffusion of information requires prior exposure: we cannot disseminate what we do not see. In online platforms, information exposure results from a complex interaction between social and algorithmic forms of curation that shapes what users see in their feeds. In this talk, I will discuss research using social media data that investigates the role algorithms play in determining the information we encounter. This research analyzes exposure to news during the US 2020 election using aggregated data for 208 million US Facebook users. The analyses compare the inventory of all political news that users could have seen in their feeds with the information that they saw (after algorithmic curation) and the information with which they engaged.