@InProceedings{wang-lan-wu:2017:SemEval,
  author    = {Wang, Feixiang  and  Lan, Man  and  Wu, Yuanbin},
  title     = {ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models},
  booktitle = {Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {491--496},
  abstract  = {This paper describes our submissions to task 8 in SemEval 2017, i.e.,
	Determining
	rumour veracity and support for rumours. Given a rumoured tweet and a lot of
	reply tweets, the subtask A is to label whether these tweets are support, deny,
	query or comment, and the subtask B aims to predict the veracity (i.e., true,
	false, and unverified) with a confidence (in range of 0-1) of the given
	rumoured tweet. For both subtasks, we adopted supervised machine learning
	methods, incorporating rich features. Since training data is imbalanced, we
	specifically designed a two-step classifier to address subtask A .},
  url       = {http://www.aclweb.org/anthology/S17-2086}
}