Sidelines: An Algorithm for Increasing Diversity in News and Opinion Aggregators
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The document presents the 'Sidelines' algorithm designed to enhance diversity in news and opinion aggregators by enabling approval voting, allowing users to support multiple articles and promoting proportional representation of various viewpoints. It discusses pilot testing results, indicating that the algorithm improves user inclusion and reduces alienation compared to traditional popularity-based systems. Future work includes refining the algorithm and exploring user preferences for diverse versus affirming content.
Editor's Notes
- #5: If people do not feel represented – they feel they have not been heard, and they don’t see content that supports them -- they may exit to places where they do. This can create balkanization and polarization. Sunstein and others have warned about the problems this may cause for democracy, society.
- #7: Make people feel represented can encourage people to speak up (who would have otherwise remained silent to promote social harmony). People may also be more open to hearing other views after they feel they have been heard.Proportional representation of viewpoints. As Duncan mentioned yesterday, people are not very good at knowing when others agree or disagree with them. They tend to think that support for their point of view is broader than it is. Those in the minority may think they are in the majority, and when their candidate does not win or their idea is not selected, they may feel disenfranchised or concoct conspiracy theories about how the election was “rigged” or “stolen.” Proportionally representing ideas can help people realize when they are in the minority. This can increase legitimacy of public decisions. It also may encourage the majority to stop and listen to dissenting views.Finally, exposing everyone to challenging viewpoints can lead to better problem solving as more ideas and viewpoints are included in the conversation. It can also help reduce polarization.
- #8: Not saying that approval voting is exactly what anyone uses!
- #18: Exclusion is just 1-inclusion.
- #19: S_alienation normalized by the maximum alienation so it always falls on the range [1/(|K|+1), 1]
- #20: Actually do need to know something about user groups and affiliations for this metric.
- #28: Blogs of one bias more likely to link to items linked by blogs with the same bias (Jaccard similarity).
- #30: Remember that the alienation score as how far down the list people had to go on average. So, a little over half the time, they didn't get an item at all. When they did which counted as an alienation of 1. The other half the time, on average, they had to go about 30-40% of the way down the list.
- #31: Subjects were recruited primarily from the university of Michigan and were somewhat liberally biased.
- #33: Need to do more work in this area of actual user preferences and behavior with respect to diversity.
- #35: The Obama administration used Google Moderator on Change.gov during the transition to collect questions. In one category, most of the top questions were about the legalization of marijuana. Sidelines may have let the stop question still be on this topic while letting other questions also make it to the first few questions.
- #36: Complement to content analysis approaches.
- #37: So we have an algorithm for increasing diversity without knowledge about content or voters’ political affiliations, and some potential applications for this algorithm. We also have some metrics for measuring diversity in result sets where people have voted on the candidate items. What’s next?Enhancement:suppress votes based on users’ voting history, optimize parametersOther algorithms:clustering based on votes