Algorithms personalize social media feeds by ranking posts from the inventory of a user’s network. However, the combination of network structure and user activity can distort the perceived popularity of user traits in the network well before any personalization step. To measure this “exposure bias” and how users might perceive their network when subjected to personalization, we conducted an analysis using archival X (formerly Twitter) data with a fixed inventory. We compare different ways recommender systems rank-order feeds: by recency, by popularity, based one the expected probability of engagement, and random sorting. Our results suggest that users who are subject to simpler algorithmic feeds experience significantly higher exposure bias compared to those with chronologically-sorted, popularity-sorted and deep-learning recommender models. Furthermore, we identify two key factors for bias mitigation: the effective degree-attribute correlation and session length. These factors can be adjusted to control the level of exposure bias experienced by users. To conclude we describe how this framework can extend to other platforms. Our findings highlight how the interactions between social networks and algorithmic curation shape—and distort—user’s online experience.