Expressive Data: Conceptualizing Political Expression Through Social Media Feeds

In an increasingly algorithmic digital landscape, everyday user data can take on expressive qualities, particularly in shaping the political content that people encounter on social media. This study examines the role of user data in political expression by focusing on social media feeds. I conducted a total of 55 interviews with 21 participants over the course of the 2020 U.S. presidential election, utilizing a technique that I call feed analysis interviewing to examine how participants understood and leveraged personal data to manage their exposure to online political content during a major political transition. Emergent findings suggest that participants considered their social media feeds as potent, if often inaccurate, reflections of their political beliefs and values, inviting reflection on political self-concept. Through this analysis, I extended conceptualizations of political expression by developing the action-identification-audience framework, composed of three indicators of political expression on social media. These include degrees of action in sharing political opinions online; identification with expressed political views; and alignment with imagined audiences, which may encompass networked connections, platform algorithms, and the future self. In addition to providing a conceptual framework for political expression on social media that can be applied in other contexts, this study identifies a novel dimension of the concept. Distributed political expression describes how individuals voice political opinions through reflexive engagement with their own data. This study offers implications for further research on political expression and media exposure at the juncture of political communication and critical data studies.