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

Nava Tintarev, Emily Sullivan, Dror Guldin, Sihang Qiu, Daan Odjik

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationUMAP 2018 - Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery, Inc
Number of pages7
ISBN (Electronic)9781450357845
ISBN (Print)9781450357845
Publication statusPublished - 2 Jul 2018
Externally publishedYes


  • Diversity based ranking
  • Framing
  • News recommendation

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