- Film, art and other cultural mediations also lend themselves to the analysis of unknown and hidden aspects of technoculture, an approach that is often used in humanities-oriented media studies that seek to address the experiential dimensions of technologies. See, for example, Hansen (2004) on the phenomenology of new media or, more recently, Hansen (2012) on the micro perception of code and ubiquitous computing. Recently, Lisa Parks and others have also called for a critical study of media infrastructure that focuses on making the materiality of infrastructure more visible by paying atention to images, art and film (Parks, 2007; Parks & Starosielski, 2015; Sandvig, 2013). For an understanding of computers through art and literature, see, for example, Hayles (2010).
- Depicting algorithms has always been a representational challenge whether it is flowcharts, diagrams, visualizations, introductory computer science textbooks, or commercial depictions (Sandvig, 2015). See also A. Galloway (2011) on the limits of representation in data science.
- For more on the role of popular imagery as representational practice, see Kirby (2011).
- Both the names and the exact wording of the tweets have been slightly changed to protect the privacy of the participants in the study reported on in this chapter.
- Although the term “ordinary people” is both contested and highly ambiguous, the prefix “ordinary” is simply used to denote the aim of the study, which was to try and talk to people who are neither computer specialists nor social media marketers but, on the face of it, have no specialist knowledge of or any obvious vested interest in algorithms.
- I conducted similar searches for the following platforms and services: Twiter, Instagram, YouTube, Amazon, Google, OkCupid, Tinder, and Spotify. While users tweet about algorithms in connection with all of these platforms, most of the searches have been conducted on Facebook and algorithms, Netflix and algorithms, and Twiter and algorithms because they were the platforms that seemed to solicit the most consistent streams of user responses. The main period of data collection regarding the Twiter searches took place during a ninemonth period stretching from October 2014 through June 2015.
- All 25 participants are pseudonymized, whereas their real age, country of residence, and occupation are disclosed. Australia: Steven (24, graphic designer). Canada: Jolene (22, fashion blogger), Nora (20, student), Larry (23, works in television), Anthony (64, art professor), Richard (41, manual laborer), Alex (age unknown, occupation unknown). Norway: Sarah (33, biologist). Philippines: Louis (20s, former student, current occupation unknown). United Kingdom: Jacob (38, on leave from a university degree). United States: Amber (25, student), Kayla (23, student), Michael (21, Musician), Rachel (24, journalist), Jessa (20s, journalist), Lucas (25, quality assurance engineer), Shannon (45, career counselor), Lena (20s, graduate student), Chris (20, student), Albert (42, works in advertising), Kate (36, former school teacher), Nancy (age unknown, public policy associate), Caitlyn (30s, teacher), Robyn (age unknown, graphic designer). Unknown location, age, and occupation: Tom, John.
- The participants were recruited from the personal Facebook network of Nora, one of the participants in the Twiter study (see note 16). It was Nora’s own interest in the subject mater and ofer to distribute a request for interview participants via her own personal Facebook account that helped me recruit new participants. Together, we designed the recruitment request for participants but phrased in the way Nora would normally speak. Nora’s status update asked people to contact her privately if they “would be willing to talk to a researcher about your experience with social media, and how it shapes the type of information that you get.” Out of the 12 people expressing an interest, 10 ended up engaging in email conversations and face-to-face interviews over Skype during May and June 2016. Once again, the names provided are pseudonyms to protect the privacy of the participants. Due to Nora’s physical locality in a university town in Canada, many of the participants recruited come from a similar geographical and socioeconomic background. They are all in their early twenties, Canadian, engaged in college education, and relatively active on social media (however, none are involved in computer science or similar subjects that would entail knowledge of algorithms).
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