Online and mobile news consumption leaves digital traces that are used to personalize news supply, possibly creating filter bubbles where people are exposed to a low diversity of issues and perspectives that match their preferences. Filter bubbles can be detrimental for the role of of journalism in democracy and are therefore subject to considerable debate among academics and policymakers alike. The existence and impact of filter bubbles are difficult to study because of the need to gather the digital traces of individual news consumption; to automatically measure issues and perspectives in the consumed news content; and to combine and analyse these heterogeneous data streams.
Can we design news recommenders to nudge users towards diverse consumption of topics and perspectives? The growing role of news recommenders raises the question of how news diversity can be safeguarded in a digital news landscape. Many existing studies look at either the supply diversity of recommendations, or the effects of (decreased) exposure diversity on e.g. polarization and filter bubbles. Research on how users choose from the available supply is lacking, making it difficult to understand the relation between algorithm design and possible adverse effects on citizens.
OSD2F is a NWO PDI-SSH funded project to create digital infrastructure for data donation. See also our recent CCR article: preprint.
OPTED is a design study that lays the foundation for an infrastructure that will serve a major hub for political text analysis in Europe. The EU-funded H2020 project involves 17 research institutions organized in 10 work packages which collaboratively work on designing the building blocks of the infrastructure. Among the objectives of the infrastructure are scientific community building, the extension of text analysis tools, and learning materials for social scientists, the broader public and journalists.