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2021 Abstracts

Don’t Block Me: Toward A Foundation of Tech-Aware Copyright Practice

Presenter: Travis Skene, College of Engineering & Technology, Computer Science
Authors: Travis Skene, Charles D. Knutson
Faculty Advisor: Charles Knutson, College of Engineering & Technology, Computer Science
Institution: Utah Valley University

Unclear legal precedent relating to copyright and fair use within the context of video sharing platforms (like YouTube), has led to two phenomena, which we analyze in this research: 1) restrictive policies on the part of YouTube relating to audio sampling; and 2) a restrictive enforcement approach on the part of some music copyright holders to demonetize and even block videos that contain samples of copyrighted materials, even where use of those materials would have traditionally been legal protected by the fair use doctrine. Policies and practices are sometimes guided by the belief that when YouTubers post music samples, their actions financially injure copyright holders and musical artists. We demonstrate empirically that not only is this operational assumption demonstrably incorrect, but in fact the data show the opposite – music sharing on YouTube benefits artists economically. We first examine the shifting nature and distribution of revenue flow to musical artists as a function of time, and demonstrate that traditional models of musical revenue generation no longer apply in a world of ubiquitous Internet connectivity, nearly omnipresent video production capabilities, and media access by consumers via streaming technologies. We then examine the data relating to revenue generation for both YouTube content producers and music copyright holders in order to accurately characterize the economic dynamics at play when YouTubers sample copyrighted audio content. To illustrate the economic advantage that flows to artists from music sharing on YouTube, we analyze several well-known incidents in which musical artists have benefited financially when their music was shared, talked about, viewed, and experienced online. Finally, we propose an initial framework for a statistical model to estimate the relative cost and benefit of restrictive copyright enforcement on YouTube.