@InProceedings{schwartz-EtAl:2017:EMNLP2017,
  author    = {Schwartz, H. Andrew  and  Rouhizadeh, Masoud  and  Bishop, Michael  and  Tetlock, Philip  and  Mellers, Barbara  and  Ungar, Lyle},
  title     = {Assessing Objective Recommendation Quality through Political Forecasting},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2348--2357},
  abstract  = {Recommendations are often rated for their subjective quality, but few
	researchers have studied comment quality in terms of objective utility. We
	explore
	recommendation quality assessment with respect to both subjective (i.e.
	users’ ratings) and
	objective (i.e., did it influence? did it improve decisions?) metrics in a
	massive online geopolitical forecasting system, ultimately comparing linguistic
	characteristics of each quality metric. Using a variety of features, we predict
	all types of quality with better accuracy than the simple yet strong baseline
	of comment length. Looking at the most predictive content illustrates rater
	biases; for example, forecasters are subjectively biased in favor of comments
	mentioning business transactions or dealings as well as material things, even
	though such comments do not indeed prove any more useful objectively.
	Additionally, more complex sentence constructions, as evidenced by subordinate
	conjunctions, are characteristic of comments leading to objective improvements
	in forecasting.},
  url       = {https://www.aclweb.org/anthology/D17-1250}
}