@InProceedings{aker-derczynski-bontcheva:2017:RANLP,
  author    = {Aker, Ahmet  and  Derczynski, Leon  and  Bontcheva, Kalina},
  title     = {Simple Open Stance Classification for Rumour Analysis},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {31--39},
  abstract  = {Stance classification determines the attitude, or stance, in a (typically
	short) text. The task has powerful applications, such as the detection of fake
	news or the automatic extraction of attitudes toward entities or events in the
	media. This paper describes a surprisingly simple and efficient classification
	approach to open stance classification in Twitter, for rumour and veracity
	classification. The approach profits from a novel set of automatically
	identifiable problem-specific features, which significantly boost classifier
	accuracy and achieve above state-of-the-art results on recent benchmark
	datasets. This calls into question the value of using complex sophisticated
	models for stance classification without first doing informed feature
	extraction.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_005}
}