Between Personal and Public Interest: How Algorithmic News Recommendation Reconciles with Journalism as an Ideology

Abstract

Throughout modern history, the introduction of new technologies in journalism has challenged journalistic roles and the normative landscape of journalism, and the emergent use of personalised news recommender systems seems no exception given personalisation’s inherent democratic risk and commercial nature. However, current literature offers limited insight into how these technologies reconcile with the shared ideological norms and beliefs of journalists. This article identifies recurring tensions between journalism’s ideology and increasing commercial pressures in existing literature focusing on illustrative examples of digital technology implementations in journalism, and discusses how personalised news recommendation may recreate or mediate these tensions. The findings suggest that personalised news recommendation can facilitate journalism in service of the public by using value-sensitive algorithmic design that incorporates editorial input and nudges users to view diverse and serendipitous content. The findings additionally emphasise the importance of algorithmic transparency as a constituent element to distinguish nudging that prompts reflective behaviour from manipulation of choice, thus mediating tensions with journalism’s ideological aspirations to be fair and objective. Lastly, the paper calls for a higher level of understanding and interaction between journalists and algorithmic designers to mediate the impact on journalistic autonomy and ease the inherent transfer of editorial authority over distribution.