Assessing Viewpoint Diversity in Search Results Using Ranking Fairness Metrics
This work addresses the challenge of measuring viewpoint diversity for search engine users, but it is incremental as it builds on existing metrics and simulations without real-world application.
The paper tackled the problem of assessing viewpoint diversity in search results by using ranking fairness metrics, showing through a controlled simulation study how these metrics can be applied and interpreted to evaluate diversity.
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 paper lays out important ground work for future research to measure and assess viewpoint diversity in real search result rankings.