Compliance of Personalized Radio with Public-Service Remits

Managing Temporal Dynamics of Filter Bubbles

“…Personalization and filter bubbles have attracted much attention in the last years in social science (e.g., [6][7][8][9]), computer science (e.g., [10,11]), information systems (e.g., [12]), and law (e.g., [13,14]) from technical, regulatory and societal viewpoints, and are also the subject of controversy (e.g., [15]). There is a large body of work emphasizing the importance of filter bubbles in opinion formation and social processes, and various approaches have been proposed how to make users aware of unbalanced content consumption (e.g., [4,5,16]) or how to avoid filter bubbles (e.g., [17]). However, the number of approaches proposed to managing filter bubbles is relatively small in comparison to the attention filter bubbles receive in terms of impact, and little design knowledge is known about how to make filter bubbles manageable for the user.…”

“…There are different ways of dealing with filter bubbles. One approach is preventive -trying to avoid filter bubbles by running algorithms in the background to ensure balance and diversity (e.g., [17]). The other approach is permissive -allowing filter bubbles but giving the user control over them, allowing the user to play the corrective factor (e.g., [4,5,16,25,26]), and enabling a kind of value co-creation between the user and the recommender system.…”