Digitalization and the emergence of large amounts of media content has 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 parties such as 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. In addition, they could also reinforce the self-selection process of citizens, which in turn also poses a risk to citizens’ consumed diversity. However, the idea of diversity is in several normative theories such as the public sphere perceived as an essential prerequisite to inform citizens properly and ensure the functioning of strong democracies. Academics therefore recommend exploring alternative ideas that can mitigate these risks and promote the idea of news diversity.
In this dissertation, we take steps in that direction by examining how news organizations can incorporate diversity as a criterion in the development of recommendation algorithms and, by doing so, stimulate users to consume a diverse range of news articles. To do so, we make use of three research themes that give structure and meaning to this dissertation. These research themes are (1) news diversity as an alternative recommendation value, (2) audiences’ perceptions towards diversity-based recommendation algorithms and (3) audiences’ consumption behavior when using diversity-based recommendation algorithms. The rationale for these research themes lies in the idea to approach news algorithms from a socio-technical perspective, taking into account both the technical-conceptual aspects and social aspects of algorithms. In the first theme, we focus on these technical-conceptual aspects by conducting a systematic literature review and an interdisciplinary study on the meaning of news diversity and the different building blocks of a diversity-based algorithm. In the second and third theme, we focus on the social aspects by conducting a survey study and experimental study in which we investigate how news consumers evaluate and interact with news algorithms.
Based on these studies, we present in this dissertation for each of these research themes several interesting insights that may be relevant to different stakeholders such as scholars, policy makers, news media and even citizens. A first important insight that emerges from our systematic literature review is that there is much diversity in the conceptualizations of the concept news diversity. For example, in our study we 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 was found in our interdisciplinary study in which it became clear that the development of a diversity-based algorithm raises pertinent questions for a broad range of disciplines. These questions were not only present in the field of computer science where most recommendation algorithms are developed, but also in fields such as law, computational linguistics, and communication sciences. For computational linguistics and computer science, these questions are primarily situated in the technical elaboration that determines the accuracy and relevance of recommendation algorithms. For example, relevant content dimensions must be translated into content extraction algorithms, which is not a solved issue. The design of the recommendation algorithm must also be carefully considered, as the right balance has to be made between relevance and diversity. For law and communication sciences, in turn, the questions are more fundamental in nature. Questions such as 'which diversity dimensions are relevant to extract' or 'what is the optimal diversity outcome' are important questions, to which no unambiguous answers currently exist. Our study presents a concise overview of these discussions and also clarifies the challenges that arise with each of these topics. For academics, these challenges are particularly relevant in order to shape future research. At the same time, this study also shows that an interdisciplinary approach is required for the development of diversity-based algorithms and can even make help the development process to be more efficient and structured.
A third important insight comes from the survey study in which we shed light on the perceptions that users have towards the different news selection mechanisms that underlie news algorithms. The results of this study show that the audience has a greater preference for news selection principles belonging to the ‘content-based similarity’ news algorithm than for those belonging to the ‘collaborative similarity’ or ‘content-based diversity’ news algorithm. This result shows that when the audience has 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 ‘similarity-based’-news 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 our experimental study, we found interesting insights about how diversity-based algorithms can affect people’s news exposure behavior and perceptions. In particular, our 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, we found that people using a diversity-based news recommender did not think they read more diverse, pointing towards a so-called diversity paradox. We forward several explanations for this paradox, but mainly point 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.