Sidelines: An Algorithm for Increasing Diversity in News and Opinion Aggregators

Abstract

Aggregators rely on votes, and links to select and present subsets of the large quantity of news and opinion items generated each day. Opinion and topic diversity in the output sets can provide individual and societal benefits, but simply selecting the most popular items may not yield as much diversity as is present in the overall pool of votes and links. In this paper, we define three diversity metrics that address different dimensions of diversity: inclusion, non-alienation, and proportional representation. We then present the Sidelines algorithm – which temporarily suppresses a voter’s preferences after a preferred item has been selected – as one approach to increase the diversity of result sets. In comparison to collections of the most popular items, from user votes on Digg.com and links from a panel of political blogs, the Sidelines algorithm increased inclusion while decreasing alienation. For the blog links, a set with known political preferences, we also found that Sidelines improved proportional representation. In an online experiment using blog link data as votes, readers were more likely to find something challenging to their views in the Sidelines result sets. These findings can help build news and opinion aggregators that present users with a broader range of topics and opinions.