Algorithms, Automation, and News

Abstract

This special issue examines the growing importance of algorithms and automation in the gathering, composition, and distribution of news. It connects a long line of research on journalism and computation with scholarly and professional terrain yet to be explored. Taken as a whole, these articles share some of the noble ambitions of the pioneering publications on 'reporting algorithms', such as a desire to see computing help journalists in their watchdog role by holding power to account. However, they also go further, firstly by addressing the fuller range of technologies that computational journalism now consists of: from chatbots and recommender systems, to artificial intelligence and atomised journalism. Secondly, they advance the literature by demonstrating the increased variety of uses for these technologies, including engaging underserved audiences, selling subscriptions, and recombining and reusing content. Thirdly, they problematize computational journalism by, for example, pointing out some of the challenges inherent in applying AI to investigative journalism and in trying to preserve public service values. Fourthly, they offer suggestions for future research and practice, including by presenting a framework for developing democratic news recommenders and another that may help us think about computational journalism in a more integrated, structured manner.

“Saving Journalism from Facebook’s Death Grip”? The Implications of Content-Recommendation Platforms on Publishers and Their Audiences

“…Going out from the vast literature on the platformization of news in the past decade, this article has explored the possible implications of the business model of content-recommendation platforms on news publishers and their audiences. In essence, T/O model intensifies two trends characterizing the evolution of the news industry in the past decades (Cornia, Sehl, and Nielsen 2020 ): the blurring boundaries between editorial content and advertising in the form of sponsored content (Hardy 2021 ); and the algorithmic curation of news, which collects indirect user signals to provide personalized recommendations (Bodó 2019 , Thurman, Lewis, and Kunert 2021 ). Merging these models, T/O creates a unique autonomous space outside the news organizations’ sovereignty.…”

Putting a Human Face on the Algorithm: Co-Designing Recommender Personae to Democratize News Recommender Systems

“…Especially when social media platforms emerged, even more consumers were lured away from traditional newsmakers. However, legacy media corporations slowly adapted to the new online media environment (Van der Haak, Parks, and Castells 2012), including the use of various digital technologies to gather, produce, and distribute news (D€ orr 2016;Thurman, Lewis, and Kunert 2019).…”

Artificial intelligence and mass personalization of communication content—An ethical and literacy perspective

“…Apart from commercial appeals, the dual role of AI in personalized content production and dissemination also pertains to news and journalistic content (e.g. Bodó, 2019;Bodó et al, 2019;Ford and Hutchinson, 2019;Guzman, 2019;Helberger, 2019;Lewis et al, 2019;Milosavljević and Vobič, 2019;Thurman et al, 2019aThurman et al, , 2019b. While the first generation of news personalization incorporated receiver-initiated customization based on explicitly expressed preferences, the second generation features implicit personalization techniques building on individuals' digital profiles and indirect preference signals (Bodó, 2019;Kunert and Thurman, 2019;Thurman and Schifferes, 2012).…”