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Algorithmic copyright enforcement on YouTube: Using machine learning to understand automated decision-making at scale
Joanne Gray, Nicolas Suzor
Queensland University of Technology, Australia
This paper presents the results of an investigation of algorithmic copyright enforcement on YouTube. We use digital and computational methods to help understand the operation of automated decision-making at scale. We argue that in order to understand complex, automated systems, we require new methods and research infrastructure to understand their operation at scale, over time, and across platforms and jurisdictions.
We use YouTube takedowns as a case study to develop and test an innovative methodology for evaluating automated decision-making. First, we built technical infrastructure to obtain a random sample of 59 million YouTube videos and tested their availability two weeks after they were first published. We then used topic modeling to identify categories of videos for further analysis, and trained a machine learning classifier to categorise videos across the entire dataset. We then use statistical analysis (multinomial logistic regression) to examine the characteristics of videos that are most likely to be removed through DMCA notices, Content ID removals, and Terms of Service enforcement.
This interdisciplinary work provides the methodological base for further experimentation with the use of deep neural nets to enable large-scale analysis of the operation of automated systems in the realm of digital media. We hope that this work will improve understanding of a useful and fruitful set of methods to interrogate pressing public policy research questions in the context of content moderation and automated decision-making.
2:20pm - 2:40pm
Spotify and Netflix as innovations: Streaming media history in the light of innovation theory
Department of Linguistic, Literary, and Aesthetic Studies, University of Bergen
Streaming services such as Spotify and Netflix have taken over large portions of the market for music and audiovisual entertainment worldwide. Some have described our current time as the “age of streaming», but the events that led us here has not yet been charted. Most writers who have studied streaming services have included some parts of their history, but the different industries have not yet been compared.This paper is a study of the history of streaming media services under the lens of innovation theory. In this ongoing study, we collect and systematize the findings of earlier published histories of streaming technology. These are contextualised with other genral histories of computer development. We find that streaming media is not one innovation, but a collection of many. Two of the most important events are Steve Jobs' ability to negotiate with all major record companies, and the introduction of "pirate" networks such as Napster, Gnutella and Pirate Bay. Counter to many popular characterisations, streaming services are not examples of disruptions in Christensen's terms, but long awaited systemic changes involving technology, economy, rights management and user patterns, including piracy practices.
2:40pm - 3:00pm
TV’s Social Media Laboratory: Audience Expertise and Dynamics of Digital Knowledge Production in the American Television Industry
University of Michigan, United States of America
Although social media work in television is broadly articulated to promotion within industrial structures and these workers primarily view themselves as marketers, the function they serve is far more complex. In their daily tasks of interacting with audience members on social media and then using a variety of analytics tools to reflect on those interactions, they produce a great deal of knowledge about the audience in near real-time. Despite their frequently young ages and low positions in the corporate hierarchy, they are often looked at as audience experts by others in the organization. This paper draws on a series of in-depth, semi-structured interviews (n=23) conducted in 2017 with social media professionals working in the American television industry at a variety of organizations (broadcast/cable networks, streaming services, advertising agencies, and analytics start-ups/consultancies) as well as supplemental industry trade press and whitepapers. In this paper, I map out some of the dynamics of how industrial knowledge about audiences is produced by social media workers, and what broader implications it has for the industry's digital future.