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.

政治运动越来越依靠Facebook来吸引选民，特别是通过广告定位。Facebook的商业模式基于保证将广告商与“合适的”用户（可能点击，下载，参与和购买的用户）联系起来的前提。该公司（通过部分算法）通过算法从数据中推断用户的兴趣，并为广告客户提供一种根据其推断的兴趣来定位用户的手段，从而实现了这一承诺。在本研究中，我们将探索此兴趣分类系统为谁服务，以便在关键数据研究的对话基础上进一步探讨此类系统如何产生基于权力结构的世界的知识。通过用户数据，我们从各种经验角度对分类系统进行了批判性分析。Facebook文档，培训和专利；以及适用于广告客户的Facebook工具-以及来自各个领域的理论概念。在此，我们重点关注分类系统如何塑造政治代表和声音的可能性，尤其是对于有色人种，妇女和LGBTQ +人。我们认为，这种“大数据驱动”的分类系统应该被理解为政治性的：它不仅阐明了在美国公共领域中哪些问题重要或不重要的立场，而且还阐明了谁被认为是足够重要的公众立场。充分说明。