@InProceedings{huang-EtAl:2017:DDDSM,
  author    = {Huang, Yi-Jie  and  Su, Chu Hsien  and  Chang, Yi-Chun  and  Ting, Tseng-Hsin  and  Fu, Tzu-Yuan  and  Wang, Rou-Min  and  Dai, Hong-Jie  and  Chang, Yung-Chun  and  Jonnagaddala, Jitendra  and  Hsu, Wen-Lian},
  title     = {Incorporating Dependency Trees Improve Identification of Pregnant Women on Social Media Platforms},
  booktitle = {Proceedings of the International Workshop on Digital Disease Detection using Social Media 2017 (DDDSM-2017)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Association for Computational Linguistics},
  pages     = {26--32},
  abstract  = {The increasing popularity of social media lead users to share enormous
	information on the internet. This information has various application like, it
	can be used to develop models to understand or predict user behavior on social
	media platforms. For example, few online retailers have studied the shopping
	patterns to predict shopper’s pregnancy stage. Another interesting
	application is to use the social media platforms to analyze users’
	health-related information. In this study, we developed a tree kernel-based
	model to classify tweets conveying pregnancy related information using this
	corpus. The developed pregnancy classification model achieved an accuracy of
	0.847 and an F-score of 0.565. A new corpus from popular social media platform
	Twitter was developed for the purpose of this study.  In future, we would like
	to improve this corpus by reducing noise such as retweets.},
  url       = {http://www.aclweb.org/anthology/W17-5804}
}