Quantitative social media research has traditionally been conducted from what might be called a platform-centric view, wherein researchers sample, collect, and analyze data based on one or more topic- or user-specific keywords. Such studies have yielded many valuable insights, but they convey little about individual users’ tailored social media environments—what I call the user-eye view. Studies that investigate social media from a user-eye view are relatively rare because of the expense involved and a limited number of suitable tools. This talk introduces PIEGraph, a novel system for user-eye view research that offers key advantages over existing systems. PIEGraph is lightweight, scalable, open-source, OS-independent, and collects Twitter data viewable from mobile and desktop interfaces directly from APIs. The system incorporates an extensible taxonomy that allows for straightforward classification of a wide range of political, social, and cultural phenomena. The presentation will focus on how our research team is using PIEGraph to examine the extent to which high- (academic/scientific/journalistic) and low-quality (disinformation/hyperpartisan) information sources populate users’ personalized information environments across lines of gender, race, ideology, and conspiracy belief.