Sidelines:
An Algorithm for Increasing Diversity in News and Opinion Aggregators
Sean Munson, Daniel Xiaodan Zhou, Paul Resnick. Sidelines: An Algorithm for Increasing Diversity in News and Opinion Aggregators. Proceedings of ICWSM09 Conference on Weblogs and Social Media. San Jose, CA.
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, nonalienation, 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.