
The fragmentation of consumption and algorithms’ increasing impact on how content is recommended and displayed makes it even more important to analyse and promote exposure diversity, i.e., the extent to which audiences are exposed to, discover, and engage with diverse content. Although there is a growing literature addressing how to define media diversity in the context of the challenges posed by platformisation, this article translates the normative dimensions into a framework for operationalising exposure diversity into a tangible policy goal, taking into account datafication and its consequences in terms of increasing data requirements towards platforms. The main objective of this study is to analyse initiatives to assess exposure diversity in the platform era and to discuss how such assessment could be improved, particularly for policy initiatives. This involves addressing several challenges of existing approaches for the assessment of exposure diversity related to defining an appropriate frame of reference, determining the degree of diversity required, dealing with data transparency issues, and promoting user autonomy. To achieve this, we propose a framework for analysing initiatives aimed at assessing and promoting exposure to media diversity. Our framework is composed of four key features: measures (type of initiative), metrics (quantifying exposure diversity), data collection methods, and data requirements. We apply this framework to a set of 13 initiatives and find that policy initiatives can benefit from adopting metrics based on distances and experimenting with data collection methods.
data acquisition, online platforms, Medien, news policy, audiovisual policy; data requirement; exposure diversity; media pluralism; news policy; online platforms; platform regulation; recommender systems, Social sciences, sociology, anthropology, Erhebungstechniken und Analysetechniken der Sozialwissenschaften, data requirement, Sozialwissenschaften, Soziologie, algorithm, media pluralism, media, Communication. Mass media, Media Pluralism, P87-96, Pluralismus, Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods, Algorithmus, Datenerfassung, pluralism, exposure diversity, platform regulation, recommender systems, audiovisual policy, ddc: ddc:300
data acquisition, online platforms, Medien, news policy, audiovisual policy; data requirement; exposure diversity; media pluralism; news policy; online platforms; platform regulation; recommender systems, Social sciences, sociology, anthropology, Erhebungstechniken und Analysetechniken der Sozialwissenschaften, data requirement, Sozialwissenschaften, Soziologie, algorithm, media pluralism, media, Communication. Mass media, Media Pluralism, P87-96, Pluralismus, Methods and Techniques of Data Collection and Data Analysis, Statistical Methods, Computer Methods, Algorithmus, Datenerfassung, pluralism, exposure diversity, platform regulation, recommender systems, audiovisual policy, ddc: ddc:300
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 4 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
