Selected Papers of #AoIR2022: The 23rd Annual Conference of the Association of Internet Researchers Dublin, Ireland / 2-5 Nov 2022 THE NETFLIX MACHINE: HOW EUROPEAN SCREEN WORKERS INTERPRET AND INTERACT WITH STREAMING DATA Nina Vindum Rasmussen London School of Economics and Political Science What does the rise of algorithms and data analytics mean for European screen production? This paper examines the experiences of screenwriters, producers, and directors (hereafter ‘screen workers’) who have collaborated with data-driven streamers like Netflix and Amazon. Only a few existing studies analyse the platformisation and datafication of screen labour (e.g. Chow, 2020; Navar-Gill, 2020). Looking ‘sideways’ to empirical findings in other areas has therefore been crucial to the design of this study (e.g. Bishop, 2019; Bonini & Gandini, 2019; Christin, 2020; Cunningham & Craig, 2021; Poell, Nieborg & Duffy, 2022). This paper contributes to such discussions about the platformisation of cultural production, and it does so with novel insights from the European screen industry. The significance of the research especially results from the way it grounds current industry-level changes in screen workers’ experiences ‘on the ground.’ The study sits at the intersection between media industry studies, production studies, critical data studies, and critical algorithm studies. As such, the paper also makes a theoretical contribution by bringing together different and cognate bodies of knowledge in order to fully illuminate this topic. A next-generation production study This project takes the form of a production study that examines how screen workers negotiate the production dynamics in a streaming era. Their meaning-making activities – that is, their beliefs, values, priorities, practices, and rituals – anchor the analysis. More specifically, I have interviewed 33 screen workers and carried out what Sherry Ortner (2010) terms an ‘interface ethnography.’ According to Ortner, this method involves attending events in which the industry presents itself to ‘the public.’ The narratives shared by screen workers and streaming executives in these public settings have departed significantly from the ones my participants have told me in confidential interviews. That leads me to a key methodological point: Accessing screen worker accounts has been very difficult, especially due to the non-disclosure agreements (NDAs) they have signed with streamers. As one participant reflects: ‘I don’t want Netflix to be in a position to feel that I’ve been, you know, disclosing too much. (…) At the Suggested Citation (APA): Rasmussen, N. (2022, November). The Netflix Machine: How European Screen Workers Interpret and Interact with Streaming Data. Paper presented at AoIR 2022: The 23rd Annual Conference of the Association of Internet Researchers. Dublin, Ireland: AoIR. Retrieved from http://spir.aoir.org. same time, I think this needs to go public. This is such an important part of the business that has such an impact.’ The quote shows why we need creative ways to overcome barriers to access. Consequently, this paper also provides practical insights into the ways scholars can engage empirically with data and algorithms in cultural production. Interpretations and interactions It is well established that streamers like Netflix and Amazon hold audience figures and insights close to their chest. The secrecy takes different forms, including the lack of viewing data shared with creators and the strict NDAs they have to sign to secure a deal. This paper demonstrates some of the ways screen workers interpret and interact with data despite this secrecy. I split up my participants’ interpretations into a typology that considers different ‘facets of data’ and their associated ‘form and function.’ These interpretations illuminate a particular kind of ‘algorithmic imaginary’ (Bucher, 2017) and ‘data imaginary’ (Beer, 2019), i.e. how people imagine, experience, and perceive these phenomena in the social world. Many of their understandings reveal more qualitative approaches to data, which forces us to look beyond narrow quantitative and technical readings of what ‘data-driven screen production’ means. With this typology in place, I then turn to the four types of interactions I have identified in my interviews: accessing, sensing, generating, and resisting data. These interactions vary in detail and scope according to screen workers’ status. Showrunners (someone who is both the head writer and executive producer of a TV show) get more access than workers who ‘only’ get a screenwriter, producer, or director credit. It also depends on one’s experience and the level of trust established with the streamer in question. Being let in on ‘the secret’ often functions as a reward. All these interactions with data include examples of what I call ‘counter-data,’ which are insights (both qualitative and quantitative) that screen workers place in opposition to the big data analytics deployed by streamers and platforms. These data can be created and utilised in more or less systematic ways. A number of participants refer to tacit forms of insights such as exchanging experiences with colleagues. Screen workers use these counter-data to understand and engage with streamers that employ data secrecy as their modus operandi. As such, they function as a kind of ‘guerrilla tactic,’ which signals the unequal power structures at play – a central theme at this year’s AoIR. Overall, these interpretations and interactions illuminate how screen workers make sense of their labour conditions and content output in a streaming era. My findings echo existing studies on creative labour, especially with regard to the ambivalence associated with doing work in creative sectors. While there are many similarities with previous research, my study also shows that the growing presence of global streamers produces new ambiguities. Striking a deal with a streamer brings significant benefits, especially when it comes to their early commitment to the project. However, several participants feel that the streamers’ tight deadlines put pressure on their work-life balance and mental health. We can also determine a lot about the look, feel, and quality of streaming content by attending to the compromises screen workers have to make as a result of changing labour conditions in a data-driven screen landscape. In short, my paper exemplifies a next-generation production study of a screen landscape increasingly powered by data and algorithms. References Beer, D. (2019). The Data Gaze: Capitalism, Power and Perception. London: SAGE. https://dx.doi.org/10.4135/9781526463210 Bishop, S. (2019). ‘Managing Visibility on YouTube Through Algorithmic Gossip’. New Media & Society, 21(11-12), 2589-2606. https://doi.org/10.1177/1461444819854731 Bonini, T. & Gandini, A. (2019). ‘“First Week Is Editorial, Second Week Is Algorithmic”: Platform Gatekeepers and the Platformization of Music Curation’. Social Media + Society, 5(4), 1-11. https://doi.org/10.1177/2056305119880006 Bucher, T. (2017). ‘The Algorithmic Imaginary: Exploring the Ordinary Affects of Facebook Algorithms’. Information, Communication & Society, 20(1), 30-44. https://doi.org/10.1080/1369118X.2016.1154086 Chow, P.-S. (2020). ‘Ghost in the (Hollywood) Machine: Emergent Applications of Artificial Intelligence in the Film Industry’. NECSUS European Journal of Media Studies, 9(1), 193-214. https://doi.org/10.25969/mediarep/14307 Christin, A. (2020). Metrics at Work: Journalism and the Contested Meaning of Algorithms. Princeton, NJ and Oxford: Princeton University Press. Cunningham, S. & Craig, D. (Eds.). (2021). Creator Culture: An Introduction to Global Social Media Entertainment. New York, NY: New York University Press. https://doi.org/10.18574/9781479890118 Navar-Gill, A. (2020). ‘The Golden Ratio of Algorithms to Artists? Streaming Services and the Platformization of Creativity in American Television Production’. Social Media + Society, 6(3), 1-11. https://doi.org/10.1177/2056305120940701 Ortner, S. B. (2010). ‘Access: Reflections on Studying Up in Hollywood’. Ethnography, 11(2), 211-233. https://doi.org/10.1177/1466138110362006 Poell, T., Nieborg, D. & Duffy, B. E. (2022). Platforms and Cultural Production. Cambridge: Polity Press.