Mirroring journalistic work in newsroom metrics: A longitudinal study of Estonian journalists’ making sense of data

While a substantial body of research has explored the intricate relationship between journalists and metrics, there remains a notable gap in systematically understanding how evolving metrics over time influence journalists’ use of data as feedback. This study addresses this gap by investigating how alterations in the most valued metrics influence the utilisation of audience data for reflection and feedback in national and local newsrooms. The study spans multiple waves from 2012 to 2023 and comprises 72 interviews with Estonian journalists. It employs the Data Loop framework to analyse the circulation of audience data among media institutions, audiences, newsrooms, and individual journalists’ reflexive actions. Examining data reflexivity within newsrooms involves considering the interplay between newsroom culture, metrics-gathering technology, external pressures from changing business models, and journalistic agency. Over time, the metrics valued by newsroom managers have shifted – from prioritising clicks as a measure of audience interest to rewarding journalists based on minutes spent on stories or the acquisition of subscriptions. In this article, we argue that instead of considering the audiences behind the data, journalists and editors mostly use the data offered by metrics to reflect on their work. Noteworthy trends include journalists conforming to data dominance and shaping their work to align with prevailing metrics, particularly in the context of paywalls where individual article readership can be linked with direct income through subscriptions. Contextualising newsroom data within the Data Loop, this study illustrates how data serves as a source for reflections at both the individual and newsroom culture levels. Despite assertions of an audience turn, our findings indicate that a focus on metrics does not necessarily signify a focus on the audience but rather an unwavering interest in journalistic production.