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Algorithms and the Streaming Wars: The Changing Meanings of Film and Television Culture

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thesis
posted on 24.05.2021, 06:38 by Niko Pajkovic
The film and television industry has been transformed by a new wave of over-the-top (OTT) video streaming services. Disney+, Apple TV Plus, NBC Universal’s Peacock, WarnerMedia’s HBO Max, and Quibi have all been released between November 12th, 2019 and May 27th, 2020, ushering in what the media has called the “Streaming Wars”. Like Netflix, Amazon, and Hulu, these platforms are dependent on the use of algorithms and Big Data, meaning the presence of these technologies within the industry will become increasingly pervasive, important, and unavoidable for producers and consumers alike. The purpose of this MRP is twofold: 1) to explore the current role algorithms play in the production, distribution and consumption of film and television, and to assess how these technologies are impacting broader notions of creativity and taste within the industry; and 2) to challenge the dominant critical theoretical perspectives that have emerged in regards to algorithmic cultures, namely, those contending that algorithms are replacing the fundamentally human process of cultural meaning- and decision-making. To achieve this, I explore the role algorithms play in the production and creative development of film and television, focusing my analysis on the emergence of data-driven creativity. I examine several third-party AI and analytics firms whose services automate the creative practices of ideation, script development, and casting. In addition, I examine how algorithms are changing the distribution and consumption of film and television via recommendation systems, and contribute to the existing dialogue regarding their implications on taste and taste-making. For that purpose, I apply Bucher’s (2018) method of reverse engineering to the Netflix Recommender System (NRS), revealing its circular and economic logics.

History

Language

eng

Degree

Master of Arts

Program

Professional Communication

Granting Institution

Ryerson University

LAC Thesis Type

Thesis