DISSERTATION - Can algorithms make us better informed?


doctoraat Glen Joris

Digitalization and the emergence of large amounts of media content have pushed organizations towards the use of algorithms to (semi-)automatically determine how information should be filtered, ranked and sorted. Especially in the news environment, there is an evolution ongoing in which news organizations increasingly rely on recommendation algorithms to personalize the news offer and tailor it to the users’ preferences. Although there are several commercial benefits related to the use of recommendation algorithms, several scholars and policy makers are concerned about how these technologies are used and designed. They believe that recommendation algorithms are a risk to citizens because they are trained to focus on similarities, between articles and people, rather than on differences. As such, they may provide more of the same news and expose citizens to a lesser extent to the diversity that is present in the news supply. Academics therefore recommend exploring alternative ideas that can mitigate these risks and promote the idea of news diversity.

In his dissertation, Glen Joris examines to what extent diversity algorithms can offer a solution to this problem. Diversity algorithms can best be understood as algorithms that do not aim to provide more of the same, but want to stimulate users to consume a more diverse news offer. The importance of such diversity algorithms has long been emphasized in the literature, but so far little is known about how these diversity algorithms can be effectively developed and also put into practice. Based on a series of communication-scientific inquiries, Glen Joris forwards several insights that may be relevant to different stakeholders.

  • A first important insight that emerges from a systematic literature review is that there is much diversity in the conceptualizations of the concept news diversity. For example, in his study, Joris found that communication scholars have used more than 43 diversity dimensions and 26 different conceptualizations to shape the concept news diversity. In addition, researchers typically focus on dimensions that are easier to measure, such as the location of the news topic or the length of an article. Dimensions that are harder to measure, such as objectivity or controversy, are generally less chosen as objects of study. Normative assumptions about news diversity are also often neglected, making it difficult to assess which ideal is dominant in the academic literature. These results are especially valuable for academia in which the concept is frequently used to assess the news landscape and where a detailed dissection of the concept was lacking. In addition, news organizations can also use these insights to reflect on their own activities and/or the development of a diversity-based algorithm.


  • A second important insight comes from the survey study in which Joris sheds light on the perceptions that users have towards the different news selection mechanisms that underlie news algorithms. The results of this study show that users have a greater preference for news selection principles that focus on a person's interests than for principles such as diversity in topics, opinions or points of view. This result shows that when users have the choice to determine how they want to receive the news, they have a tendency to prefer news articles that only interests them. To address the risks that are involved with this tendency, we forward a new approach, called ‘personalized diversity’. In this approach, the ultimate goal of the diversity algorithm remains the same, but it takes advantage of the personalization techniques that underlie commercial algorithms. This approach is particularly valuable for news organizations who want to implement the idea of diversity in existing or future recommendation activities. At the same time, it also shows that news selection principles are not mutually exclusive and are thus quite compatible with each other.


  • Finally, in an experimental study, Joris found interesting insights about how diversity-based algorithms can affect people’s news exposure behavior and perceptions. In particular, the research results show that diversity-based algorithms can steer users towards more diverse exposure behavior, with the personalized diversity-based news recommender being most effective. Moreover, Joris found that people using a diversity-based news recommender did not think they read more diverse, pointing towards a so-called diversity paradox. Joris forwards several explanations for this paradox, but mainly points in the direction of transparency and the lack thereof in recommendation systems. This result is especially valuable for policy makers, to advance discussions on the importance of transparency in recommendation systems and to take further policy actions on this issue.

This dissertation was part of the interdisciplinary research project NewsDNA that was financially supported by Ghent University (project number: BOFGOA2018000601). The first two studies are published in the scientific journals Journalism Studies and Digital Journalism.

Read the dissertation here (only UGent network) or contact Glen Joris