Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics

Conference paper (2021)

Authors

T.A. Draws Web Information Systems - EEMCS
N. Tintarev Web Information Systems - EEMCS
Ujwal Gadiraju Web Information Systems - EEMCS
A. Bozzon Human-Centred Artificial Intelligence - IDE
Benjamin Timmermans IBM Benelux
Research Group
Web Information Systems (EEMCS) (TU Delft)
Copyright: © 2021 T.A. Draws, N. Tintarev, Ujwal Gadiraju, A. Bozzon, Benjamin Timmermans
DOI
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https://doi.org/10.1145/3468507.3468515
Viewpoint diversity Web search Ranking fairness
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Published Date

2021

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Software Technology

Research Group

Web Information Systems

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.