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.
A. Vercoustre, J. Pehcevski, and J. Thom. Pre-proceedings of the sixth International Workshop of the Initiative for the Evaluation of XML Retrieval (INEX 2007), (2007)
J. Kazama, and K. Torisawa. Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, page 698--707. (2007)
S. Banerjee. ICMLA '07: Proceedings of the Sixth International Conference on Machine Learning and Applications (ICMLA 2007), page 148--153. Washington, DC, USA, IEEE Computer Society, (2007)
S. Banerjee, K. Ramanathan, and A. Gupta. SIRIR '07: Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, page 787--788. New York, NY, USA, ACM Press, (2007)