Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

Draws, T.A. (TU Delft Web Information Systems)
Tintarev, N. (TU Delft Web Information Systems)
Gadiraju, Ujwal (TU Delft Web Information Systems)
Bozzon, A. (TU Delft Human-Centred Artificial Intelligence)
Timmermans, Benjamin (IBM Benelux)

2021

The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewpoint diversity is not trivial to assess. In this paper we use existing and novel ranking fairness metrics to evaluate viewpoint diversity in search result rankings. We conduct a controlled simulation study that shows how ranking fairness metrics can be used for viewpoint diversity, how their outcome should be interpreted, and which metric is most suitable depending on the situation. This pa- per lays out important ground work for future research to measure and assess viewpoint diversity in real search result rankings.

viewpoint diversity
web search
ranking fairness

http://resolver.tudelft.nl/uuid:ba940ff1-fc77-45d5-9abb-b1afcbabb3ce

https://doi.org/10.1145/3468507.3468515

2021-12-01

ACM SIGKDD Explorations Newsletter, 23 (1)

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

conference paper

© 2021 T.A. Draws, N. Tintarev, Ujwal Gadiraju, A. Bozzon, Benjamin Timmermans