About the project
The NEWSREC project deals directly with one of the most pressing questions facing the news media today: What are the precise conditions under which news recommender technology are for the better or the worse for the democratic role of the news media? Evidence of news recommenders’ dystopic democratic threats (e.g., Filter Bubbles) and of their opportunities to counter such threats remain largely anecdotal. Despite an increasing scholarly attention to recommenders, the precise conditions under which they are a threat to or an opportunity for democracy remain a puzzle.
We will address this puzzle head-on by offering a radically new perspective: We aim to shift the scholarly attention from the dominant perspective of uncovering and describing whether the current news recommenders amplify or reduce selective exposure and sharing to understanding the conditions under which recommenders do so, given that they are designed for that purpose. By focusing on this counterfactual (i.e., what has not happened but could or might under differing conditions), we radically shift the responsibility for the democratic implications of recommenders from the technology itself to the decisions surrounding the implementation and design of the technology. We mobilize this novel perspective by developing the first news recommender that is tailor-made to pioneer research on the conditions under which news recommenders amplify or reduce selective exposure and sharing.
Project number: 324835
WP1: Building a framework
We will develop a framework for understanding when and how news recommenders can increase or decrease selective exposure and sharing.
WP2: The does and dont's of news nudging
We will delineate the ethical considerations pertaining to designing recommenders to influence selective exposure and sharing
WP3: Building a news recommender
We will develop the first news recommender system equipped with factors that increase or decrease selective exposure and sharing.
WP4: Testing the news recommender in the real world
We will use a randomized field experiment to test our news recommender system to gain a precise understanding of when and how news recommender systems increase or decrease selective exposure and sharing.