Algorithmic Nostalgia: How Gen-Z Experiences the Past in Platformed Media Ecologies

Abstract

This thesis examines how Generation-Z members (born 1997–2012) engage with experiences of nostalgia within platformed media ecologies, with particular attention to the role of algorithmic systems in cueing, shaping, and sustaining nostalgic affect. Using Reflexive Thematic Analysis with 30 individuals aged 19-23, this study identified seven analytically distinct themes that highlight the significant structural change in how this generation experiences nostalgia. Firstly, and most crucially, participants’ experiences highlight a shift in the primary source of nostalgia, at least for participants, in most instances, the platform, rather than the user, was the primary source. They are no longer using an algorithm to recall nostalgic memoriesnow, an algorithm analyses and dictates the time, space, and frequency at which the nostalgic memory occurred, creating a very different temporal experience from what was experienced prior to the use of these platforms. In addition, the users created counter-algorithmic strategies such as scarcity engineering, creating more opportunities for future analogue memories, and prioritising the use of pre-cognitive, body-based triggers. Secondly, and perhaps most importantly, the data demonstrated an unprecedented temporal paradigm shift regarding nostalgia and nostalgic feelings in the literature, in which users reported feeling nostalgia that was not due to loss that has already taken place but rather on the verge of feeling a loss, as well as experiencing nostalgia with no previous autobiographical memories. This research extends nostalgia theory into the domain of platform and algorithmic studies by reconceptualising nostalgia in the digital age as a sociotechnical process co-produced by users and algorithmic systems within defined platform ecologies. Instead, it fundamentally concerns young individuals' capacity to maintain coherent, self-authored emotional lives.