@InProceedings{hasanuzzaman-EtAl:2017:Short,
  author    = {Hasanuzzaman, Mohammed  and  Kamila, Sabyasachi  and  Kaur, Mandeep  and  Saha, Sriparna  and  Ekbal, Asif},
  title     = {Temporal Orientation of Tweets for Predicting Income of Users},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {659--665},
  abstract  = {Automatically estimating a user's socio-economic profile from their language
	use in social media can significantly help social science research and various
	downstream applications ranging from business to politics. The current paper
	presents the first study where user cognitive structure is used to build a
	predictive model of income. In particular, we first develop a classifier using
	a weakly supervised learning framework to automatically time-tag tweets as
	past, present, or future. We quantify a user's overall temporal orientation
	based on their distribution of tweets, and use it to build a predictive model
	of income. Our analysis uncovers a correlation between future temporal
	orientation and income. Finally, we measure the predictive power of future
	temporal orientation on income by performing regression.},
  url       = {http://aclweb.org/anthology/P17-2104}
}