@InProceedings{xiao-EtAl:2016:COLING,
  author    = {Xiao, Yang  and  Wang, Yuan  and  Mao, Hangyu  and  Xiao, Zhen},
  title     = {Predicting Restaurant Consumption Level through Social Media Footprints},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3328--3338},
  abstract  = {Accurate prediction of user attributes from social media is valuable for both
	social science analysis and consumer targeting.  In this paper,  we propose a
	systematic method to leverage user online social media content for predicting
	offline restaurant consumption level. We utilize the social login as a bridge
	and construct a dataset of 8,844 users who have been linked across Dianping
	(similar to Yelp) and Sina Weibo. More specifically, we construct consumption
	level ground truth based on user self report spending. We build predictive
	models using both raw features and, especially, latent features, such as topic
	distributions and celebrities clusters. The employed methods demonstrate that
	online social media content has strong predictive power for offline spending.
	Finally, combined with qualitative feature analysis, we present the differences
	in words usage, topic interests and following behavior between different
	consumption level groups.},
  url       = {http://aclweb.org/anthology/C16-1314}
}