Automated Media: An Institutional Theory Perspective on Algorithmic Media Production and Consumption

Automated MediaPhilip M. Napoli
Communication Theory

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excerpt One of the key functions that algorithms perform in contemporary media consumption is to assist audiences in the process of navigating an increasingly complex and fragmented media environment. Central to this navigation process are the typically algorithmically driven search, recommendation, and content aggregation systems that facilitate searching for and selecting content in an environment of such extreme content abundance that technologically unaided forms of search and navigation are no longer practical or effective (Anderson, 2006). These algorithmically driven systems are, of course, central to search engines, social media platforms, and content aggregators such as Amazon, iTunes, YouTube, Pandora, and Netflix. To some extent, one could argue that content has become commodified, with the real value residing in the systems that users can employ to navigate through and select from the wealth of available content. on 6/3/2019, 5:43:06 PM

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excerpt Further, the dynamics of many search, recommendation, and navigation algorithms emphasize popularity as a key criterion in generating results (see Jones, 2012; Webster, 2011), which again leads to a certain reflexivity in their operation. Popular content is what is most frequently and prominently recommended, thus further enhancing its popularity relative to other available content, and inhibiting less popular content from gaining popularity (see Cho & Roy, 2004). on 6/3/2019, 5:45:26 PM

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excerpt Many newsrooms now operate with comprehensive and immediate feedback related to various aspects of online news consumption, ranging from page views to time spent on a site/story, to ratings, to volume and valence of comments (see, e.g., Anderson, 2010; Anderson, 2011b). on 6/3/2019, 5:53:19 PM

excerpt Content farms mine search engine data to estimate demand for content on various topics, and then produce that content rapidly and cheaply in order to meet that demand (Bakker, 2012). Once again, the process is algorithmically driven. Leading content farm Demand Media, for instance, feeds its algorithm three types of data: (a) popular search terms from search engines, (b) the ad market for keywords (i.e., which keywords are currently being sought and for how much), and (c) the competitive environment (in terms of content that is already available online) (Roth, 2009). The output then represents a prediction of the type of content for which there is the highest unmet audience and advertiser demand, and Demand Media produces that content accordingly (Anderson, 2011a). on 6/3/2019, 5:54:28 PM


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C.W. Anderson via Automated Media: An Institutional Theory Perspective on Algorithmic Media Production and Consumption on 6/3/2019, 5:55:33 PM