DIRT maintains accuracy at scale because every contributor needs to deposit tokens to write data. If the data is correct, it is freely shared. If the data is incorrect, anyone can challenge the data and earn tokens for identifying these inaccurate facts. Our protocol and platform makes it economically irrational for misinformation to persist in a data set.
M. Collins, and N. Duffy. Advances in Neural Information Processing Systems 14 --- Proceedings of the 2001 Neural Information Processing Systems Conference (NIPS 2001), December 3-8, 2001, Vancouver, British Columbia, Canada, page 625--632. MIT Press, Cambridge, MA, USA, (2002)
M. Collins. Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, page 1--8. Stroudsburg, PA, USA, Association for Computational Linguistics, (2002)
J. Tang, M. Hong, J. Li, and B. Liang. International Semantic Web Conference, volume 4273 of Lecture Notes in Computer Science, page 640-653. Springer, (2006)