The Wisdom of Crowds
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The bandwagon effect of collaborative filtering technology
CHI '08 Extended Abstracts on Human Factors in Computing Systems
Harnessing the wisdom of crowds in wikipedia: quality through coordination
Proceedings of the 2008 ACM conference on Computer supported cooperative work
Is the Crowd's Wisdom Biased? A Quantitative Analysis of Three Online Communities
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Parallel prototyping leads to better design results, more divergence, and increased self-efficacy
ACM Transactions on Computer-Human Interaction (TOCHI)
Towards quality discourse in online news comments
Proceedings of the ACM 2011 conference on Computer supported cooperative work
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A system that acts as a tool to correct inaccuracies and biases in online news articles is needed to alleviate the flow of misinformation perpetuated by the fast paced nature of the Internet. We propose Maater, which counteracts these issues by leveraging crowdsourced corrections and fact checking to help other readers engaged with a particular article better understand it. The system incorporates user-generated in-line commentary and corrections, which are vetted by other readers through a ranking system. Highly ranked comments gain more social value and are prominently displayed. This provides corrections with greater prominence than they are given by news outlets.