@InProceedings{preoiucpietro-chandraguntuku-ungar:2017:EMNLP2017,
  author    = {Preoţiuc-Pietro, Daniel  and  Chandra Guntuku, Sharath  and  Ungar, Lyle},
  title     = {Controlling Human Perception of Basic User Traits},
  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     = {2335--2341},
  abstract  = {Much of our online communication is text-mediated and, lately, more common with
	automated agents. Unlike interacting with humans, these agents currently do not
	tailor their language to the type of person they are communicating to. In this
	pilot study, we measure the extent to which human perception of basic user
	trait information -- gender and age -- is controllable through text. Using
	automatic models of gender and age prediction, we estimate which tweets posted
	by a user are more likely to mis-characterize his traits. We perform multiple
	controlled crowdsourcing experiments in which we show that we can reduce the
	human prediction accuracy of gender to almost random -- an over 20\% drop in
	accuracy. Our experiments show that it is practically feasible for multiple
	applications such as text generation, text summarization or machine translation
	to be tailored to specific traits and perceived as such.},
  url       = {https://www.aclweb.org/anthology/D17-1248}
}