The algorithmic imaginary: exploring the ordinary affects of Facebook algorithms

By Taina Bucher. 2017

961 citations recorded by the source corpus.

ABSTRACT This article reflects the kinds of situations and spaces where people and algorithms meet. In what situations do people become aware of algorithms? How do they experience and make sense of these algorithms, given their often hidden and invisible nature? To what extent does an awareness of algorithms affect people's use of these platforms, if at all? To help answer these questions, this article examines people's personal stories about the Facebook algorithm through tweets and interviews with 25 ordinary users. To understand the spaces where people and algorithms meet, this article develops the notion of the algorithmic imaginary. It is argued that the algorithmic imaginary – ways of thinking about what algorithms are, what they should be and how they function – is not just productive of different moods and sensations but plays a generative role in moulding the Facebook algorithm itself. Examining how algorithms make people feel, then, seems crucial if we want to understand their social power.

Subfields: Safety Research, Sociology and Political Science, Human-Computer Interaction

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Citations in the corpus

Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “Machine learning (ML) systems increasingly influence every facet of our lives, including the quality of healthcare and education we receive [18, 34, 35, 53], which news or social media posts we see [4, 20, 91], who receives a job [50, 89], who is released from jail [8, 25], and who is subjected to i”
Data Colonialism: Rethinking Big Data’s Relation to the Contemporary Subject (2018, background): “This is a vast area, but see, for example, Gillespie (2010), Gerlitz and Helmond (2013), and Bucher (2017).”
Managing visibility on YouTube through algorithmic gossip (2019, background): “In this vein, Taina Bucher (2016) positions the black box as a ‘red herring’ (p. 94).”
Algorithms in practice: Comparing web journalism and criminal justice (2017, background): “…work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). about the practices, representations, and imaginaries of the people who rely on algorithmic technologies in their work and lives (Bucher, 2016; Gillespie, 2016; Levy, 2015).”
Anxiety, panic and self-optimization (2018, background): “Assumptions about algorithms can influence modes of self-presentation, tone of voice, choice of content covered, words and sentence structures used (Bucher, 2017).”
Towards Algorithmic Experience: Initial Efforts for Social Media Contexts (2018, methodology): “From a commercial perspective, AX may contribute towards a more joyful and faithful relationship with a service, avoiding the possible bad experiences [7,9].”
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity (2019, methodology): “Furthermore, critical algorithm studies have examined algorithms directed toward constituents outside the organization, such as platform users (Bucher 2012, 2017; Mager 2012; Willson 2017), customers (Crawford 2015), consumers (Carah 2015), or freelance workers (Kushner 2013) but less on algorithms…”
Understanding perception of algorithmic decisions: Fairness, trust, and emotion in response to algorithmic management (2018, background): “First, our research contributes to emerging studies that investigate people’s mental models and folk theories of algorithms (Bucher, 2016; Eslami et al., 2015; Rader and Gray, 2015), and social studies on automation, artiﬁcial intelligence and machine learning more broadly.”
Experiencing Algorithms: How Young People Understand, Feel About, and Engage With Algorithmic News Selection on Social Media (2021, background): “Although algorithms’ occasional oversimplifications of their interests invoked irritation, none of the interviewees had experienced effects of user profiling that were explicitly discriminating (cf. Bucher, 2017; Lomborg & Kapsch, 2019).”
Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram (2018, background): “Such information pursuits likely contribute to the algorithmic imaginary and impact the ways users direct their interactions with algorithms (Bucher, 2017; Van der Nagel, 2018).”
"Algorithms ruin everything": #RIPTwitter, Folk Theories, and Resistance to Algorithmic Change in Social Media (2017, background): “Several related lines of work have established that user folk theories can function in the place of specific knowledge of what an algorithm does [12], allowing the individual to respond to an algorithm’s perceived behavior, an arrangement that Bucher has labeled the “algorithmic imaginary” [5].”
Exposure diversity as a design principle for recommender systems (2018, background): “Nevertheless, the interests of service providers and users are not always the same – algorithmic filtering is influenced by many other interests, including advertisers ’ , so providers do not always register the ‘ real interests ’ or choices of consumers (e.g., Bucher, 2016).”
Intimate Surveillance: Normalizing Parental Monitoring and Mediation of Infants Online (2017, background): “The design, operation, and continual changing of Facebook’s algorithms, for example, are attempting to continually produce affective encounters, where the sentiment or mood is heightened to encourage further interaction and engagement (Bucher, 2017).”