Today, these ideas are no longer mere predictions of a distant future. The computerization and algorithmization of news and news work have increasingly become the norm. While many of the core tools identified by Meyers remain the same, the scale and extent to which computational tools and processes are incorporated into news work are what’s novel.
If the digitization of news once meant making analogue content digitally available, it now signifies a series of processes that go far beyond simple online newspaper distribution. The digitization and computerization of news can be seen at every stage of the production cycle: how journalists use computational tools to collect and produce news stories, the way in which systematized tagging and metadata practices are used to archive content, through the implementation of digital paywalls to ensure revenue and access to valuable user data, and in the algorithmic presentation and curation of news.
Let’s at this point recall the common understanding of an algorithm as a set of instructions for solving a problem. What are the problems that algorithms are supposed to help solve in this context? One obvious problem for journalism is the “challenge to the economic viability of newspapers triggered by the digital revolution in publishing and news distribution” (Alexander, 2015: 10). Sociologist Jefrey Alexander observes how many “leading journalistic institutions in the West have experienced great economic upheaval, cuting staf, and undergoing deep, often radical reorganization—in eforts to meet the digital challenge” (2015: 10). These cuts and reorganizations have also been felt acutely in the Nordic context, where many newspapers, including Aftenposten, are currently experiencing a dramatic reduction in staf—primarily those working with the print edition. As Alexander notes, the cultural mantra “information wants to be free” that came along with the Internet has become an economic headache for journalism today.5 Declining advertising and the rise of blogs and social media platforms as fierce market competitors have contributed significantly to the economic crisis in journalism in recent years. Rasmus Kleis Nielsen notes how over “half of all digital advertising revenues globally go to Google, Facebook” (2016: 86), and the stigma that was once atached to paywalls continues to be an obstacle for their economic and cultural feasibility (Alexander et al., 2016).
The rise of computational journalism, here broadly understood as news entangled with algorithmic practices, comes at a time of crisis not only for the newspaper industry and journalism in general, but also in terms of broader social and economic unrest. Not unlike the rhetoric surrounding the rise of CAR in the 1960s, the discourses of computational and algorithmic journalism are characterized by a continuum of utopian and dystopian visions. Algorithms, data science, and everything that comes with them are distinguished by a great degree of techno-optimism and promise, but also marked by a notable skepticism and worries about machines taking over jobs. On the optimistic side, computational journalism is talked about as a way of upgrading and equipping journalism for the demands of the 21st century (Hamilton & Turner, 2009; Cohen et al., 2011). As Lewis and Usher note, “many argue that computational journalism will both lead to beter investigative journalism and create new forms of engagement with audiences” (2013: 606). The sentiment here is that journalism needs “to adapt to technological and economic changes in order to survive” (Peters & Broersma, 2013: 2). More than just as a celebration of technical possibilities, many see these digital transformations as a changing condition of news to which one simply needs to adapt in order to stay in the game.
Programming the News
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