PROPOSING RECIPROCAL DIGITAL METHODS: A USER-CENTRIC METHOD FOR ALGORITHMIC SOCIAL PLATFORMS IN A POST-API WORLD

Abstract

This paper introduces reciprocal digital methods, a novel research framework tailored to the exigencies of studying social media in what has been called a post-API landscape (Bruns, 2019; Freelon, 2018). In this paper we build on scholarly discourse on the epistemology and ethics of social media data (Lomborg & Bechmann, 2014; Marres & Gerlitz, 2016), and the current debates about the future of social media research (Bruns, 2019; Freelon, 2018; Ohme & Araujo, 2022; Tromble, 2021). We propose a model that is intended to push the field forward, merging approaches to social media that have been largely disparate, and combining computational analysis of user-level digital trace data and interviews with the same users. We argue that user perspectives and digital trace data should not be considered as separate methods but as part of a reciprocal exchange and a broader methodological pluralism (Danermark et al., 2019). Digital data, while rich in potential insights, often lacks the context necessary to interpret user behavior and platform interaction accurately. Conversely, interviews provide depth and narrative but are generally not reliable for capturing use patterns. By combining these two elements, the proposed methodology enables researchers to bridge the gap between narrative and pattern, and between media use and media practice. Moreover, we propose that inviting users into the quantitative analysis process can help correct for the noted lack of agency users have had in big data studies (Bishop & Kant, 2023).