unbalanced information access patterns

BALANCE: Enhancing Diversity in News and Opinion Aggregators

Aggregators such as Digg, Reddit, and Google News rely on ratings and links to select and present subsets of the large quantity of news and opinion items generated each day. The goals of this research are to: 1) form alternative measures of diversity for result sets; 2) develop algorithms for selecting result sets that jointly optimize for diversity and popularity; 3) evaluate user interface techniques for presenting more diverse result sets; and 4) assess the impacts of alternative selection and presentation methods on people's willingness to use an aggregation service, their exposure to diverse opinions, and the size of their argument repertoires. The results of the project will provide a better understanding of alternative notions of what it means for a set of items to be diverse, and the range of reactions that different people have to varying levels and presentations of diversity.

BALANCE is a project at the University of Michigan's School of Information. This material is based upon work funded by National Science Foundation award #IIS-0916099.

Team members include Professor Paul Resnick, graduate students Sean Munson, Daniel Zhou, and Sidharth Chhabra, and undergraduate Brian Ford. Previous members of the project include Adam Feldman, Peter Andrews, who built a tool to visualize some of our selection algorithms during summer 2009 and wrote the first version of our Firefox extension, Erica Willar, and Cat Le.

Publications

Tools & code

We have built a Google Gadget that displays popularly linked items from political blogs, selected by the Sidelines algorithm. You can view and install it from Google's gadget directory.
Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
last updated: 14 July 2011