Same, Same, but Different
Algorithmic Diversification of Viewpoints in News
N. Tintarev (TU Delft - Web Information Systems)
E. Sullivan (TU Delft - Ethics & Philosophy of Technology)
Dror Guldin (Universiteit van Amsterdam)
S. Qui (TU Delft - Web Information Systems)
Daan Odjik (Blendle Research)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Recommender systems for news articles on social media select and filter content through automatic personalization. As a result, users are often unaware of opposing points of view, leading to informational blindspots and potentially polarized opinions. They may be aware of a topic, but only be exposed to one viewpoint on this topic. However, recommender systems have just as much potential to help users find a plurality of viewpoints. In this spirit, this paper introduces an approach to automatically identifying content that represents a wider range of opinions on a given topic. Our offline results show positive results for our distance measure with regard to diversification on topic and channel. However, our user study results confirm that user acceptance of this diversification also needs to be addressed in tandem to enable a complete solution.