Deep nostalgia: remediated memory, algorithmic nostalgia, and technological ambivalence

Jenny Kidd*, Eva Nieto Mcavoy

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Digital recreations of the past, and of the deceased, are part of the Internet's present. They circulate within social networks where logics of connection and connectivity underpin increasingly performative memory work. In this article we explore these developments through a case study of the MyHeritage deep learning feature, Deep Nostalgia. Our analysis is informed by a close critical study of Deep Nostalgia creations, and discourses circulating around them, shared on Twitter during the two-week period following its launch, February 2021 (n.6935). We examine how memory is evoked, framed, re-worked and distorted through algorithmic processes, and within social networks in particular, and explore what this tells us about peoples' need to connect with their pasts. First, we analyse how the shift from photo to video 'revives' the dead via a process that we have termed 'remediated memory'. Second, we explore the affective dimensions and resonances of Deep Nostalgia creations. In doing so, we introduce the concept of 'algorithmic nostalgia' to describe the ways nostalgia is generated, organised and exploited through Deep Nostalgia's automated and recursive algorithmic mechanisms. Third, we interrogate the ways social media logics shape the use and influence of these outputs. Our study's scholarly contribution is at the intersection of memory, automation, and algorithms. We highlight the importance of studying the ambivalence of emerging media at their nexus with memory studies and, critically, of attending to the ways corporate interests increasingly shape - and assimilate - these activities.
Original languageEnglish
Pages (from-to)620-640
Number of pages21
JournalConvergence
Volume29
Issue number3
Early online date2 Feb 2023
DOIs
Publication statusPublished - 1 Jun 2023

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