A User-Centric Approach to Content Curation in Pantry

Abstract

The shift towards personalized content consumption in social media has driven the rise of black-box algorithms which are foundational in delivering tailored experiences. These algorithms do an incredible job at delivering tailored experiences without much effort from the user, however often utilize intrusive methods such as location, watch time, and scrolling behavior. At the same time, alternative social networks have emerged with an added emphasis on decentralization, transparency, and privacy. However, a significant gap remains in this space: the absence of alternative social media applications that provide users with feed curation tools, make these tools accessible to all end-users, and unify fragmented user communities across diverse networks. This paper introduces Pantry, a social media reader application designed to address this gap through the idea of teachable feeds, inspired by existing literature, and powered by an on-device machine learning model. Evaluation through a user study reveals that Pantry succeeds in delivering feed curation tools driven by users and provide several key insights. The results of this paper, from the user study, help inform and advance the future design space.