Same, Same, but Different

Same, Same, but Different: Algorithmic Diversification of Viewpoints in News

Tintarev, N. (TU Delft Web Information Systems)
Sullivan, Emily (TU Delft Ethics & Philosophy of Technology)
Guldin, Dror (Universiteit van Amsterdam)
Qiu, S. (TU Delft Web Information Systems)
Odjik, Daan (Blendle Research)

2018

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.

News recommendation
diversity based ranking
framing

http://resolver.tudelft.nl/uuid:fb5a2111-27e6-46a8-9ef8-c14036439d9a

https://doi.org/10.1145/3213586.3226203

Association for Computing Machinery (ACM), New York, NY

978-1-4503-5784-5

UMAP '18 Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

UMAP 2018, 2018-07-08 → 2018-07-11, Singapore, Singapore

Accepted author manuscript

Institutional Repository

conference paper

© 2018 N. Tintarev, Emily Sullivan, Dror Guldin, S. Qiu, Daan Odjik