@InProceedings{thorne-EtAl:2017:NLPmJ,
  author    = {Thorne, James  and  Chen, Mingjie  and  Myrianthous, Giorgos  and  Pu, Jiashu  and  Wang, Xiaoxuan  and  Vlachos, Andreas},
  title     = {Fake news stance detection using stacked ensemble of classifiers},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {80--83},
  abstract  = {Fake news has become a hotly debated topic in journalism. In this paper, we
	present our entry to the 2017 Fake News Challenge which models the detection of
	fake news as a stance classification task that finished in 11th place on the
	leader board. Our entry is an ensemble system of classifiers developed by
	students in the context of their coursework.  We show how we used the stacking
	ensemble method for this purpose and obtained improvements in classification
	accuracy  exceeding each of the individual models' performance on the
	development data. Finally, we discuss aspects of the experimental setup of the
	challenge.},
  url       = {http://www.aclweb.org/anthology/W17-4214}
}