Turning Filter Bubbles into Bubblesphere with Multi-Viewpoint KOS and Diverse Similarity

The filter bubble phenomenon and its negative societal effects have been extensively explored in the literature in the past decade. However, the ability of modern AI-based systems to create personalized information bubbles, that is, to classify similar contents and users into clusters according to their interests and behavior, can actually be quite beneficial if utilized and managed properly and ethically. In this article we present ongoing research that aims to refine such bubble-building smart systems by adopting an ethical, multi-perspective approach that allows for linking isolated bubbles into a consolidated bubblesphere and offering users a choice to explore diverse bubbles related to their topics of interest. To implement the proposed approach, content matching should be based on diverse similarity, which can be derived from a multi-viewpoint KOS. In addition, the study explores how such a multi-viewpoint KOS and bubblesphere can be constructed using Wikidata's ranks and qualifiers.