Political campaigns increasingly rely on Facebook for reaching their constituents, particularly through ad targeting. Facebook’s business model is premised on a promise to connect advertisers with the “right” users: those likely to click, download, engage, purchase. The company pursues this promise (in part) by algorithmically inferring users’ interests from their data and providing advertisers with a means of targeting users by their inferred interests. In this study, we explore for whom this interest classification system works in order to build on conversations in critical data studies about the ways such systems produce knowledge about the world rooted in power structures. We critically analyze the classification system from a variety of empirical vantage points—via user data; Facebook documentation, training, and patents; and Facebook’s tools for advertisers—and through theoretical concepts from a variety of domains. In this, we focus on the ways the classification system shapes possibilities for political representation and voice, particularly for people of color, women, and LGBTQ+ people. We argue that this “big data-driven” classification system should be read as political: it articulates a stance not only on what issues are or are not important in the U.S. public sphere, but also on who is considered a significant enough public to be adequately accounted for.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Information Systems and Management
- Library and Information Sciences