As social media platforms have developed over the past decade, they are no longer simply sites for interactions and networked sociality; they also now facilitate backwards glances to previous times, moments, and events. Users’ past content is turned into definable objects that can be scored, rated, and resurfaced as “memories.” There is, then, a need to understand how metrics have come to shape digital and social media memory practices, and how the relationship between memory, data, and metrics can be further understood. This article seeks to outline some of the relations between social media, metrics, and memory. It examines how metrics shape remembrance of the past within social media. Drawing on qualitative interviews as well as focus group data, the article examines the ways in which metrics are implicated in memory making and memory practices. This article explores the effect of social media “likes” on people’s memory attachments and emotional associations with the past. The article then examines how memory features incentivize users to keep remembering through accumulation. It also examines how numerating engagements leads to a sense of competition in how the digital past is approached and experienced. Finally, the article explores the tensions that arise in quantifying people’s engagements with their memories. This article proposes the notion of quantified nostalgia in order to examine how metrics are variously performative in memory making, and how regimes of ordinary measures can figure in the engagement and reconstruction of the digital past in multiple ways.

Machine-learning algorithms have become deeply embedded in contemporary society. As such, ample attention has been paid to the contents, biases, and underlying assumptions of the training datasets that many algorithmic models are trained on. Yet, what happens when algorithms are trained on data that are not real, but instead data that are ‘synthetic’, not referring to real persons, objects, or events? Increasingly, synthetic data are being incorporated into the training of machine-learning algorithms for use in various societal domains. There is currently little understanding, however, of the role played by and the ethicopolitical implications of synthetic training data for machine-learning algorithms. In this article, I explore the politics of synthetic data through two central aspects: first, synthetic data promise to emerge as a rich source of exposure to variability for the algorithm. Second, the paper explores how synthetic data promise to place algorithms beyond the realm of risk. I propose that an analysis of these two areas will help us better understand the ways in which machine-learning algorithms are envisioned in the light of synthetic data, but also how synthetic training data actively reconfigure the conditions of possibility for machine learning in contemporary society.

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Computer science tends to foreclose the reading of its texts by social science and humanities scholars – via code and scale, mathematics, black box opacities, secret or proprietary models. Yet, when computer science papers are read in order to better understand what machine learning means for societies, a form of reading is brought to bear that is not primarily about excavating the hidden meaning of a text or exposing underlying truths about science. Not strictly reading to make sense or to discern definitive meaning of computer science texts, reading is an engagement with the sense-making and meaning-making that takes place. We propose a strategy for reading computer science that is attentive to the act of reading itself, that stays close to the difficulty involved in all forms of reading, and that works with the text as already properly belonging to the ethico-politics that this difficulty engenders. Addressing a series of three “reading problems” – genre, readability, and meaning – we discuss machine learning textbooks and papers as sites where today's algorithmic models are actively giving accounts of their paradigmatic worldview. Much more than matters of technical definition or proof of concept, texts are sites where concepts are forged and contested. In our times, when the political application of AI and machine learning is so commonly geared to settle or predict difficult societal problems in advance, a reading strategy must open the gaps and difficulties of that which cannot be settled or resolved.

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.