Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
T.A. Draws (TU Delft - Web Information Systems)
N. Tintarev (TU Delft - Web Information Systems)
Ujwal Gadiraju (TU Delft - Web Information Systems)
A. Bozzon (TU Delft - Human-Centred Artificial Intelligence)
Benjamin Timmermans (IBM Benelux)
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Abstract
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.