@InProceedings{zamani-schwartz:2017:EACLshort,
  author    = {Zamani, Mohammadzaman  and  Schwartz, H. Andrew},
  title     = {Using Twitter Language to Predict the Real Estate Market},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {28--33},
  abstract  = {We explore whether social media can provide a window into community real
	estate -foreclosure rates and price changes- beyond that of traditional
	economic and demographic variables. We find language use in Twitter not only
	predicts real estate outcomes as well as traditional variables across counties,
	but that including Twitter language in traditional models leads to a
	significant improvement (e.g. from Pearson r = :50 to r = :59 for price
	changes). We overcome the challenge of the relative sparsity and noise in
	Twitter language variables by showing that training on the residual error of
	the traditional models leads to more accurate overall assessments. Finally, we
	discover that it is Twitter language related to business (e.g. 'company',
	'marketing') and technology (e.g. 'technology', 'internet'), among
	others, that yield predictive power over economics.},
  url       = {http://www.aclweb.org/anthology/E17-2005}
}