“News organisations all over the world have in recent years increased their use of analytics – systematic analysis of quantitative data on various aspects of audience behaviour aimed at growing audiences, increasing engagement, and improving newsroom workflows.” (Cherubini & Nielsen, 2016, p.7).
Last month, BBC announced an initiative to use machine learning to learn what the public wants to watch. Their goal is to create a personal BBC for readers in order to entertain in new ways. They will work with data scientists and experts from UK universities, media and tech companies to accomplish this initiative. User data will be analysed and algorithms applied to get insight into audience preferences. Detailed information about who looks to content, and when and why they do so, as well as their attitude, become part of the new machine learning algorithm. It is not odd that BBC takes this initiative, “because news organisations today are competing for attention in an ever-more competitive and constantly changing media environment” (Cherubini & Nielsen, 2016, p.9).
According to Edson and Thomas (2015) there are three concerns about the use of data in journalism (p.244). First, the danger of viewing the audience as disaggregated segments based on consumer preference. Second, journalism must provide the public with more than just what the public wants. Finally, the dangers of romanticizing the audience (idem). With the use of audience data, the public has more control of the newsroom and thus potentially sets the newsroom (p.264). Only when journalists are aware of the concerns and they take along their own judgement, journalism won’t be only news stories that score well, but also stories that are valuable to our society.
Cherubini, Federica, and Rasmus Kleis Nielsen. Editorial Analytics: How News Media Are Developing and Using Audience Data and Metrics. Oxford, UK: Reuters Institute for the Study of Journalism, 2016. Web.
Tandoc, Edson C., and Ryan J. Thomas. “The Ethics of Web Analytics.” Digital Journalism 3.2 (2015): 243–258. Taylor and Francis+NEJM. Web.