by Cory Barker
In the future, I believe that computer learning will move to be more interactive and start to become even more closer aligned with the ideas of artificial intelligence.
Specifically, these reflections above offered insight that narrowcasting and Netflix’s practices were the beginnings of a new standard of production in mass media. However, other participants were uncertain if these trends would eventually be positive or negative.
I believe that computer learning will only continue to grow and evolve for better or for worse. Maybe at some point the government will get involved and limit how computer learning is being used and developed. I also see any type of evolution of this computer learning to actually take some time and possibly longer than my own life span.
Computers will be able to predict and learn things that were never possible before. It is going to change the way content is created and presented to the user, and hopefully will be put to good use to impact online video streaming in a positive way.
Except no matter what it’s being used for, it will always be a double-edged sword. Safety could turn into an invasion of privacy and exciting recommendations can turn into the annoyance of being more figured out than you thought. But I do think machine learning will continue to get more detailed and individualized in the future. I also think it will just link together and use information from all of the devices a person uses, more so than it does already.
Netflix was also compared to other companies; illustrating how these same practices may be used or are already in use by other media organizations.
I think computer learning will really advance in the future, like … how Amazon is now delivering with drones in California, if it is a success it probably become worldwide in the matter of time. Computers will become faster and “smarter.” I don’t know how many times I’ve ended on YouTube longer than I originally intended because I just kept clicking more of the recommended, similar videos in the sidebar. I guess there is a chance that this technology could go too far and learn too much about our personal life but right now I can’t really think of any technology that could be that extreme.
Overall, I think the learning technology of computers and things of the like will only get more accurate, and implemented into more platforms. It helps websites and advertisers target the consumer, especially since as a society, we constantly find ways to avoid advertising. We install Adblock on our browsers, DVR shows so we can fast forward through television ads, or get Netflix so we don’t have to spend $20 at the movies plus sit through all of those previews. Advertisers have to capture our attention because otherwise we will find a way around it, so this type of learning helps them find out exactly what we want to see so maybe we will sit through it and hear their message.
The expansion of the scope of the journals is particularly interesting, as participants were only directly asked about their interactions with Netflix. Their inclusion of other businesses and industries suggests that the millennial participants viewed narrowcasting as a larger social and institutional trend, rather than something unique to Netflix.
Finally, many participants referenced other events, films, and pop culture in an effort to describe their hesitations about how these trends might evolve over time:
I really do think computer learning will continue in the future for a long time, even when we move to different platforms. They will become so advanced with AI, but will never have agency, or the human aspect of learning. It makes me think of the Terminator movies, where Skynet becomes self-aware and turns into actual intelligence. At that point we start playing God and question what actual intelligence is, with our without the human aspect. Even robots people are making are moving in that direction. They don’t have to be full-size, human replicas. I saw on the Science Channel a couple days ago about body suits that help people with disabilities regain their mobility. Those suits move based on sensors in the body. I know what was a bit of a tangent, but it’s still similar to computer learning.
It is the culmination of these quotes and journal entries that present a complicated look at how millennials view the future of Netflix, the larger media industry, and narrowcasting. Most participants recognized Netflix’s practices and methods of data collection and linked them to larger social trends such as the reduction of privacy, growth of computer/machine learning, and even artificial intelligence. It is these connections that are perhaps most relevant to a reflection on Netflix, because of the often negative tone and criticism often levied by participants at these growing industry trends.
Reflections
The millennial participants in this study were highly aware of the technological platform and techniques employed by Netflix to collect user data and provide recommendations. The insight provided in these journals suggests that not only are participants aware of the techniques, they see potential positive and negative consequences in both the short- and long-term. While the previous section detailed the discourses found within the journals, this section will elaborate on the meaning of these findings and provide contextual information to for future research.
Despite the open-ended prompt, millennial participants relayed a nearly uniform means of reflecting on Netflix and its narrowcasting techniques. As mentioned previously, many participants identified and described the techniques and technical process used to collect information about users and provide recommendations. Most participants not only identified the process, but also were able to use the language of narrowcasting and big data analysis to do so. Terms such as “computer-learning,” “artificial intelligence,” and “data mining” suggest that participants are well versed in these techniques. While the term narrowcasting was never used in the journal entries, participants correctly identified the process and techniques behind it. Even those who were not exactly sure of the process of Netflix and its narrowcasted recommendations understood that they were achieved by processes of tracking and monitoring. This is critical because previous research has suggested that millennials are unaware of the processes used to collect digital data and the role it has in their everyday lives. Contrary, this study suggests that not only are millennial participants aware of these procedures, they can also identify how it influences their user experiences.
This may also give insight into why participants frequently noted that they favored the Netflix platform over other online streaming competitors such as Hulu. Netflix’s personalization process, despite its lack of public information about user data, was said to be more understandable than other media platforms. This is particularly clear in the journals’ connections to other media platforms, companies, and even film analogies. While there is still uncertainty associated with Netflix, there is also some confidence with the description of its process. This perhaps explains the popularity of the platform among millennials, although much more research will need to be conducted to support this hypothesis.
Findings on the user experience and future predictions provide us with examples of how millennials view the role of the narrowcasting process moving forward. While most participants recognized there were some positives, including saving time, having a more meaningful media experience, and tailoring digital media options to personal preferences, more participants argued that the Netflix platform represented a long-term negative turn for the role of technology in everyday lives. Again, unlike previous literature that suggests millennials were born into a digital environment and thus are less critical of digital platforms and consequences, these participants demonstrated that they were both aware and concerned regarding the impact that these technologies may have on the future.
Furthermore, although millennials are identified as being concerned with the implications technology may have on their own lives and not on society in general, these participants nearly all reflected on how Netflix’s practices are indicative of larger trends—and other companies—and may potentially negatively impact other people. While more quantitative work would need to be done to generalize these findings to the larger millennial populat
ion, this does suggest that effects are viewed as both individual and social within the Netflix platform.
Because most participants identified the processes used by Netflix to create recommendations, they could also then critique the smaller technological elements that made up the larger effects. For example, four participants identified that many of the techniques used on Netflix are also the foundations of artificial intelligence technologies. Therefore, the journal was utilized as a means for looking at larger trends in digital data and technology. As with previous research, millennial participants in this study failed to take a hard stance on whether these technologies would have a positive or negative overall effect on either individual users or larger society. While not generalizable to the entire millennial population, these findings provide insight into the mindset of the generation. For example, 20 participants identified how narrowcasting may have both pros and cons (the remaining seven did not discuss effects). However, this may be more reflective of the journal prompt than an actual finding regarding the millennial generation. The prompt asked participants to reflect on their experience after looking over their Netflix account. As a result, this implies that they would find both positive and negative aspects to the platform.
Recruitment is also one possible reason for these findings. University Institutional Review Board policies on this project only allow for limited data collection using journal analysis. This means the analysis cannot make larger conclusions regarding gender, location, major, language, citizenship, or time with Netflix account. These are all considerations that need to be placed in the future. Other future research should identify how other age groups interact with Netflix and narrowcasting on other media platforms. Many participants here argued that sites such as Amazon, Hulu, and Facebook similarly integrate user content into recommendations. However, other methods such as focus groups or in-depth interviews should also be considered to clarify some of the original themes found within this study.
The role of narrowcasting in creating programing and platforms for targeted audiences is a growing trend in the digital media industry. As a result, it is critical to understand how various audiences react to these evolving norms. The millennial generation, often viewed as narcissistic and lacking critical skills, not only recognized narrowcasting as a part of the Netflix platform but also had strong opinions regarding its long-term consequences in society. While not a generalizable finding, this seriously challenges previous estimates and theories regarding the influence of narrowcasting on young people and the digital media environment.
Journal Prompt
Thank you for participating in this journal study on Netflix. Below you will see a journal prompt. Please type your journal entry in the space below. When you have finished your submission, please click “complete” at the bottom of the screen. This will submit your entry and conclude your participation in the study.
Before starting, login to your Netflix account and look at the recommendations for genres, titles, or actors. Consider how well they fit your interests, viewing history, or preferences. In the space below consider and answer the questions in narrative form. Use any examples, experiences, or other content to support your answer.
How well do Netflix’s recommendations fit you? How do you think Netflix makes these recommendations or knows about viewing interests? Are these recommendations a positive or negative aspect of the site?
NOTES
1. “Netflix Prize,” Netflix, n.d., accessed October 1, 2014, http://www.netflixprize.com/rules.
2. Tom Vanderbilt, “The Science Behind the Netflix Algorithms That Decide What You’ll Watch Next,” Wired, August 7, 2013, accessed September 1, 2014, http://www.wired.com/2013/08/qq_netflix-algorithm/.
3. Suchan Chae and Daniel Flores, “Broadcasting versus Narrowcasting,” Information, Economics, and Policy 10 (1998): 45.
4. Vanderbilt, “The Science Behind the Netflix Algorithms.”
5. Ibid.
6. Ibid.
7. Blake Hallinan and Ted Striphas, “Recommended for You: The Netflix Prize and the Production of Algorithmic Culture, New Media & Society 18.1 (2014): 117–137.
8. Tony Cheng-Kui Huang, Ing-Long Wu, Chih-Chung Chou, “Investigating Use Continuance of Data Mining Tools,” International Journal of Information Management 33.5 (2013): 791–801.
9. Steve Lohr, “Netflix Cancels Contest After Concerns Are Raised About Privacy,” New York Times, March 12, 2010, accessed October 1, 2014, http://www.nytimes.com/2010/03/13/technology/13netflix.html.
10. Dustin D. Berger, “Balancing Consumer Privacy with Behavioral Targeting,” Santa Clara Computer & High Technology Law Journal 27.1 (2010): 3–21.
11. Felix Salmon, “Netflix’s Dumbed-Down Algorithms,” Reuters, January 3, 2014, accessed June 10, 2014, http://blogs.reuters.com/felix-salmon/2014/01/03/netflixs-dumbed-down-algorithms/.
12. Megan Mullen, “The Rise and Fall of Cable Narrowcasting,” Convergence: The International Journal of Research into New Media Technologies 8.2 (2002): 65.
13. Ibid.
14. Beretta E. Smith-Shomade, “Narrowcasting in the New World Information Order: A Space for the Audience?” Television and New Media 5 (2004): 75.
15. Ibid.
16. Susan Tyler Eastman, Sydney W. Head, and Lewis Klein. Broadcast/Cable Pro-gramming: Strategies and Practices (Belmont, CA: Wadsworth, 1985).
17. Ibid.
18. Smith-Shomade, “Narrowcasting in the New World Information Order,” 75.
19. Eileen Meehan, “Why We Don’t Count: The Commodity Audience,” in Logics of Television: Essays in Cultural Criticism, ed. Patricia Mellencamp (Bloomington: Indiana University Press, 1990), 117–37.
20. Smith-Shomade, “Narrowcasting in the New World Information Order,” 75.
21. Syed M. Khatih, “The Exclusionary Mass Media: ‘Narrowcasting’ Keeps Cultures Apart,” Black Issues in Higher Education 13.11 (1996): 26.
22. Peter Ludes, Convergence and Fragmentation: Media Technology and the Information Society (New York: Intellect Books, 2008).
23. R. Kelly Garrett and Paul Resnick, “Resisting Political Fragmentation on the Internet,” Daedalus 4 (2011): 108.
24. Ibid.
25. Zvezdan Vukanovic, “Global Paradigm Shift: Strategic Management of New and Digital Media in New and Digital Economics,” International Journal on Media Management 11 (2009): 200.
26. Philip N. Howard, “Deep Democracy, Thin Citizenship: The Impact of Digital Media in Political Campaign Strategy,” The Annals of the American Academy of Political and Social Science 597 (2005): 153.
27. Zvezdan Vukanovic, “Global Paradigm Shift,” 81.
28. Ibid.
29. Helen Wood, “Television Is Happening: Methodological Considerations for Capturing Digital Television Reception,” European Journal of Cultural Studies 10.4 (2007): 485.
30. “Narrowcasting Revenues Expected to Triple by 2009,” TechWeb, 2009, accessed June 10, 2014, https://business.highbeam.com/138350/article-1G1–127480743/narrowcasting-revenues-expected-triple-2009-revenues.
31. “Research and Markets Offers Report on ‘2007 Trends to Watch: Media & Broadcasting Technology,’” Marketsensus, 2007, accessed June 10 2014, http://marketsensus.com/2007-trends-to-watch-media-broadcasting-technology.
32. Jennifer Gillan, Television and New Media: Must-Click TV (New York: Routledge, 2010).
33. “Advertising and Marketing Companies: How Audience Size Affects Word of Mouth,” Marketing Weekly News, April 2010, accessed June 10, 2014, http://www.mckinsey.com/insights/marketing_sales/a_new_way_to_measure_word-of-mouth_marketing.
34. D.T.Z. Mindich, Tuned Out: Why Americans Under 40 Don’t Follow the News (New York: Oxford University Press, 2005).
35. Debora S. Vidali, “Millennial Encounters with Mainstream Television News: Excess, Void, and Points of Engagement,” Linguistic Anthropology 10 (2010): 275.
36. Alison N. Novak, “Millennials, Citizenship, and How I Met Your Mother,” in Parasocial Politics:
Audience, Pop Culture, and Politics, ed. Jason Zenor (New York: Lexington Books, 2014), 200.
37. Nick Couldry, Sonia Livingstone, and Tim Markham, Media Consumption and Public Engagement: Beyond the Presumption of Attention (London: Palgrave MacMillan, 2007).
38. Laura Harvey, “Intimate Reflections: Private Diaries in Qualitative Research,” Qualitative Research 11 (2011): 664–684.
39. Niall Bolger, Angelina Davis, and Eshkol Rafaeli, “Diary Methods: Capturing Life as It Is Lived,” Annual Review of Psychology 54.1 (2003): 579–619.
From Interactive Digital Television to Internet “Instant” Television
Netflix, Shifts in Power and Emerging Audience Practices from an Evolutionary Perspective
VIVI THEODOROPOULOU
This essay examines users’ experiences with Netflix, focusing on the emerging viewing habits, consumption patterns, and preferences of early adopters in the United Kingdom, and seeks to identify what these users appreciate and dislike about streaming video technology. More pointedly, this essay relates Netflix early adopters to initial users of Sky Digital, a pioneering interactive satellite digital television (DTV) service of the early 2000s and a Netflix precursor of sorts. Promoted with the slogan “Watch what you want, when you want”—one almost identical to Netflix’s messaging today—Sky Digital quickly grew into the most popular UK DTV service before being overtaken by Freeview, the terrestrial DTV operator, in 2007.1 Through a comparison of Netflix and Sky Digital, the essay illustrates how television and its audience are changing, how users respond to digital content, and the ways in which new habits develop around technological features offered by popular content services.
