News Recommender Systems and News Diversity, Two of a Kind? A Case Study from a Small Media Market
Abstract
:1. Introduction
2. Literature Review
2.1. News Diversity and Democratic Societies
2.2. News Recommender Systems and Democratic Societies
- Liberal recommenders in which the autonomy and rights of citizens to receive information is safeguarded (Eskens et al. 2017).
- Participatory recommenders in which citizens are actively nudged, coached and engaged to try, for instance, political news content.
- Deliberative recommenders that ‘can present the greatest opportunities to the democratic process, as well as the most profound threats’ (Helberger 2019, p. 1005), and ‘could give particular visibility to public service media content’ (ibid., p. 1006).
3. Research Design
4. Results
‘We have to align different strategic objectives of different news titles. That is why websites and apps of our similar Dutch and Flemish news brands (e.g., quality and popular titles) look very similar, as the technological backend software and recommender systems are developed at the company level as opposed to the brand level. This facilitates reaching similar objectives per type of news title.’(Digital product owner, commercial media)
‘An algorithm that works in the Netherlands, is not destined to work in Flanders as well, not to mention Wallonia. If there would be an algorithm for personalized news, I do wonder to what extent it is applicable to all news titles across countries. I can perfectly imagine that algorithms for similar titles wouldn’t correspond as expected, because they are operating from different countries and markets.’(Online news journalist, commercial media)
‘It might sound a bit idealistic, but journalists should devote their time to reporting from their own expertise, without caring that much about how many people read or watch their pieces. That are worries for editors or news managers, who are best equipped to maintain the healthy balance between various topics.’(Online news journalist, commercial media)
‘We see that the broadcasting function of our brands remains vital. People visit a news website or app to instantly see what the biggest stories of the day are. This hampers far-reaching initiatives to personalize news.’(Data analyst, commercial media)
‘Our goal is to implement recommender systems cross-brand. If someone watches a documentary about global warming at our streaming service, that person will see recommendations for recent news articles about the topic as well. We call that the public service algorithm.’(Digital product owner, public service media)
‘We’re not Netflix. News content is more sensitive because of editorial independence. In widening news brands through verticals, it is easier to forego this independence as they are not directly linked to news content. We don’t fill our homepages yet with personalization, as we consider that as a giant leap forward which needs more discussions with editors-in-chiefs and directors.’(Digital brand manager, commercial media)
‘Reaching wide portions of society is very important to PSM. We find that people are very interested in news, but that it is often not available tailor-made for them and their needs. Segmentation has been a recurring practice for decades, in which we ‘translate’ radio news to the audiences of our various radio channels. They use different, relatable approaches, yet the biggest news stories themselves remain the same everywhere.’(Editor-in-chief, public service media)
‘When you are logged in at our app, we know who and where our users are. We can then send them, for instance, segmented newsletters or push notifications to their devices, based on the region they live in.’(Editor-in-chief, public service media)
5. Discussion and Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Conflicts of Interest
Appendix A
# | Media | Function Title (Anonymized) |
---|---|---|
1 | Commercial | Online Journalist |
2 | Commercial | Online Journalist |
3 | Public service | Online Journalist |
4 | Commercial | Digital Product Owner |
5 | Commercial | Digital Product Owner |
6 | Public service | Digital Product Owner |
7 | Commercial | Editor-in-Chief |
8 | Public service | Editor-in-Chief |
9 | Commercial | Data Analyst |
10 | Commercial | Technologist |
11 | Commercial | Technology Provider |
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Hendrickx, J.; Smets, A.; Ballon, P. News Recommender Systems and News Diversity, Two of a Kind? A Case Study from a Small Media Market. Journal. Media 2021, 2, 515-528. https://doi.org/10.3390/journalmedia2030031
Hendrickx J, Smets A, Ballon P. News Recommender Systems and News Diversity, Two of a Kind? A Case Study from a Small Media Market. Journalism and Media. 2021; 2(3):515-528. https://doi.org/10.3390/journalmedia2030031
Chicago/Turabian StyleHendrickx, Jonathan, Annelien Smets, and Pieter Ballon. 2021. "News Recommender Systems and News Diversity, Two of a Kind? A Case Study from a Small Media Market" Journalism and Media 2, no. 3: 515-528. https://doi.org/10.3390/journalmedia2030031
APA StyleHendrickx, J., Smets, A., & Ballon, P. (2021). News Recommender Systems and News Diversity, Two of a Kind? A Case Study from a Small Media Market. Journalism and Media, 2(3), 515-528. https://doi.org/10.3390/journalmedia2030031