This article investigates the information landscape shaped by curation algorithms that seek to maximize user engagement. Leveraging unique behavioral data, we trained machine learning models to predict user engagement with tweets. Our study reveals how the pursuit of engagement maximization skews content visibility, favoring posts similar to previously engaged content while downplaying alternative perspectives. The empirical grounding of our work contributes to the understanding of human-machine interactions and provides a basis for evidence-based policies aimed at promoting responsible social media platforms.