Dr Benjamin Jacobsen benjamin.jacobsen@durham.ac.uk
Post Doctoral Research Associate
When is the right time to remember?: Social media memories, temporality and the kairologic
Jacobsen, Benjamin N
Authors
Abstract
This article asks what impact temporality and timing have on the ways in which memories are felt and made to matter on social media. Drawing on Taina Bucher’s theorisation of the ‘kairologic’ of algorithmic media, I explore how digital memories are resurfaced or made visible to people at the ‘right time’ in the present. The article proposes the notion of ‘right-time memories’ to examine the ways in which social media platforms and timing performatively shape people’s engagement with the past. Drawing on interview and focus group data, I explore four ways that right-time memories are sociotechnically produced and felt in everyday life: through an anniversary logic, personalisation, rhythms, and tensions. Ultimately, it is argued that when memories are made to matter in the present is a crucial way to further examine the temporal politics of social media platforms and algorithms.
Citation
Jacobsen, B. N. (2022). When is the right time to remember?: Social media memories, temporality and the kairologic. New Media and Society, https://doi.org/10.1177/14614448221096768
Journal Article Type | Article |
---|---|
Online Publication Date | Jun 3, 2022 |
Publication Date | Jun 3, 2022 |
Deposit Date | Jul 19, 2022 |
Publicly Available Date | Jul 19, 2022 |
Journal | New Media & Society |
Print ISSN | 1461-4448 |
Electronic ISSN | 1461-7315 |
Publisher | SAGE Publications |
Peer Reviewed | Peer Reviewed |
DOI | https://doi.org/10.1177/14614448221096768 |
Public URL | https://durham-repository.worktribe.com/output/1197210 |
Files
Published Journal Article
(187 Kb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
You might also like
Machine learning, meaning making: On reading computer science texts
(2023)
Journal Article
Machine learning and the politics of synthetic data
(2023)
Journal Article
‘You Can’t Delete a Memory’: Managing the Data Past on Social Media in Everyday Life
(2022)
Journal Article
The tensions of deepfakes
(2023)
Journal Article
Regimes of recognition on algorithmic media
(2021)
Journal Article
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search