3 research outputs found
Automated journalism in UK local newsrooms: attitudes, integration, impact
Automated journalism is increasingly used to produce News content in UK local newsrooms. Although scholars have been discussing the disruptive potential of automation for journalism, little is known about how local media practitioners deploy and perceive automated journalism. This study aims to help fill this research gap using semi-structured interviews with media practitioners from four local news companies. Each use automated content provided by the news automation service RADAR moderated by human journalists at RADAR itself. Typically RADAR identifies important national datasets on release and then uses a human journalist to create an algorithm to analyse the data for local variations. This material is then made available through a subscription service to the end user, the local newsroom. It is for the local newsroom teams to decide what is relevant to their audiences.
Our findings show that local journalists evaluate automated journalism based on several occupational influences, that they integrate RADAR’s automated journalism into their own editorial outputs in various ways, and that the use of this automated journalism is having an impacts on shaping local nes agendas and newsroom performance. Our evidence also shows that whilst most media practitioners perceive a limited relevance of automated journalism for local news reporting and continue to stress the importance of human agency in the journalism workflow, what they report is conversely a shift in their practices which actually suggests that automated journalism has greater impact than they are currently willing to acknowledge
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Exploring audience perceptions of, and preferences for, data-driven ‘quantitative’ journalism
Although data-driven ‘quantitative' journalism has increased in volume and visibility, little is known about how it is perceived and evaluated by audiences. This study helps fill this research gap by analysing the characteristics of quantitative journalism that a diverse group of 31 news consumers pay attention to and, within those characteristics, where their preferences might lie. In eight group interviews, participants read and discussed articles chosen to represent the diversity that exists in the forms and production of data-driven journalism. Our analysis reveals 28 perception criteria that we group into four major categories: antecedents of perception, emotional and cognitive impacts, article composition, and news and editorial values. Several criteria have not been used in prior research on the perception of quantitative journalism. Our criteria have obvious application in future research on how audiences perceive different types of quantitative journalism, including that produced with the help of automation. The criteria will be of interest too for researchers studying audience perceptions and evaluations of news in general. For journalists and others communicating with numbers, our findings indicate what audiences might want from data-driven journalism, including that it is constructive, concise, provides analysis, has a human angle, and includes visual elements
Making local public interest journalism viable again: an opportunity for human computer interaction?
Can AI offer local journalism a lifeline in a competitive market with declining resources