We Are The Data: How We Are Shaping Netflix


[I]f World War II helped to usher in the era of so-called Big Science, the new millennium has arrived as the era of Big Data. (Gitelman, 2016:168)

How Netflix catalogues, suggests, and creates content is a study in “dataveillance” in which we are the unwitting resource for data collection (ibid: 173). This is commonplace: “Facebook is not your friend, it is a surveillance engine” says Richard Stallman (Srinivasan & Kandavel, 2012), while Zuckerberg declares that Google, Microsoft and Yahoo all collect user data surreptitiously: (project00video, 2011: at 46:08).

So how does Netflix use you to create content and make suggestions?

We know what you played, searched for, or rated, as well as the time, date, and device. We even track user interactions such as browsing or scrolling behavior. (Vanderbilt, 2013)

This is far more than cataloguing watched content; it is a descriptor of how you watch, when you watch, fast-forward and rewind, and items selected for content detail even if you decide against viewing. They may scrap user-generated 5-star rating as users rate how they think a product should be perceived rather than whether they actually enjoyed it (MacAlone, 2016). This data is also being used to generate new content:

The human element is important to the company; they catalogue content with a 40-strong team noting 100+ genre variables per film. This method gives them a count of 76,897 micro-genres:

Netflix has meticulously analyzed and tagged every movie and TV show imaginable. They possess a stockpile of data about Hollywood entertainment that is absolutely unprecedented. (Madrigal, 2014)

This allows a detailed cross-referencing of success toward the content creation, but is Netflix’s confirmation bias repacking Facebook’s New Feed ‘bubble’? Is it culturally wise to just give me what I like, rather than avail me of other content?

Tony Pomfret


Chun, WHK and Fisher, AW with Keenan, T (Eds) (2016) New Media, Old Media: A History and Theory Reader 2nd Ed. New York; London: Routledge

Gitelman, L (2016) “Raw Data” is an oxymoron. In: Chun, WHK and Fisher, AW with Keenan, T (Eds) (2016) New Media, Old Media: A History and Theory Reader 2nd Ed. New York; London: Routledge (pp167-176)

MacAlone, N (2016) Netflix want to ditch its 5-star rating system. UK Business Insider. Available at: http://uk.businessinsider.com/netflix-wants-to-ditch-5-star-ratings-2016-1?r=US&IR=T (Accessed 18th Feb 2017)

Madrigal, AC (2014) How Netflix Reverse Engineered Hollywood. The Atlantic. Available at: https://www.theatlantic.com/technology/archive/2014/01/how-netflix-reverse-engineered-hollywood/282679/ (Accessed 18th Feb 2017)

project00video (2011) Charlie Rose – Interview with Facebook Leadership Mark Zuckerberg, Ceo, Sheryl Sandberg, Coo. YouTube. Available at: https://www.youtube.com/watch?v=eqxNtEc4rzc (Accessed 19th Feb 2017)

Srinivasan, S & Kandavel, S (2012) Facebook is a surveillance engine, not your friend: Richard Stallman, Free Software Foundation. Economic Times. Available at: http://economictimes.indiatimes.com/opinion/interviews/facebook-is-a-surveillance-engine-not-friend-richard-stallman-free-software-foundation/articleshow/11786007.cms (Accessed 19th Feb 2017)

Vanderbilt, T (2013) The Science Behind The Netflix Algorithms That Decide What You’ll Watch Next. Wired. Available at: https://www.wired.com/2013/08/qq_netflix-algorithm/ (Accessed 18th Feb 2017)

Wernicke, S (2015) How to use data to make a hit TV show. TED. Available at: https://www.ted.com/talks/sebastian_wernicke_how_to_use_data_to_make_a_hit_tv_show (Accessed 18th Feb 2017)

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