When Algorithms Recommend What’s New(s): New Dynamics of Decision-Making and Autonomy in Newsgathering

Hannes Cools,Baldwin Van Gorp,Michaël Opgenhaffen

doi:10.17645/mac.v9i4.4173

Abstract

Newsroom innovation labs have been created over the last ten years to develop algorithmic news recommenders (ANR) that suggest and summarise what news is. Although these ANRs are still in an early stage and have not yet been implemented in the entire newsroom, they have the potential to change how newsworkers fulfil their daily decisions (gatekeeping) and autonomy in setting the agenda (agenda-setting). First, this study focuses on the new dynamics of the ANR and how it potentially influences the newsworkers’ role of gatekeeping within the newsgathering process. Second, this study investigates how the dynamics of an ANR could influence the autonomy of the newsworkers’ role as media agenda setters. In order to advance our understanding of the changing dynamics of gatekeeping and agenda-setting in the newsroom, this study conducts expert interviews with 16 members of newsroom innovation labs of The Washington Post, The Wall Street Journal, Der Spiegel, the BBC, and the Bayerische Rundfunk (BR) radio station. The results show that when newsworkers interact with ANRs, they rely on suggestions and summaries to evaluate what is newsworthy, especially when there is a “news peak” (elections, a worldwide pandemic, etc.). With regard to the agenda-setting role, the newsworker still has full autonomy, but the ANR creates a “positive acceleration effect” on how certain topics are put on the agenda.

Highlights

Newsroom innovation labs have been created over the last ten years to develop algorithmic news recom‐ menders (ANR) that suggest and summarise what news is
That is why in this study we focus on the impact of these ANRs by conducting in‐ depth interviews with newsroom innovation lab employ‐ ees at The Washington Post, The Wall Street Journal, Der Spiegel, the BBC, and the Bayerische Rundfunk (BR) radio station
Since we want to map out which ANR are present in the various newsrooms of our sample, and since these ANRs are related to the results, we will give a brief overview of the ANR here

Summary

Introduction

Newsroom innovation labs have been created over the last ten years to develop algorithmic news recom‐ menders (ANR) that suggest and summarise what news is. With the help of intelligent technologies, these algo‐ rithmic recommenders are increasingly being deployed in the news ecosystem, where tools such as Mode (e.g., translating and restructuring stories) and Starfruit (e.g., summarising news stories) are used to make news rec‐ ommendations based on data (Beam & Kosicki, 2014; Molumby, 2020; Nechushtai & Lewis, 2019; Ricci et al, 2011). Some point out that these ANRs and recommendations may lead to a decrease in the quality of the news on offer, or result in more polarisation (Helberger, 2019; Pariser, 2011). Others point to the positive repercussions of the implementation of such ANRs, as they can result in finding new angles or cause more inter‐ action with readers (Beckett, 2019).

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Conclusion