@InProceedings{skeppstedt-kerren-stede:2017:DDDSM,
  author    = {Skeppstedt, Maria  and  Kerren, Andreas  and  Stede, Manfred},
  title     = {Automatic detection of stance towards vaccination in online discussion forums},
  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     = {1--8},
  abstract  = {A classifier for automatic detection of stance towards vaccination in online
	forums was trained and evaluated. Debate posts from six discussion threads on
	the British parental website Mumsnet were manually annotated for stance
	'against' or 'for' vaccination, or as 'undecided'.  A support vector machine,
	trained to detect the three classes, achieved a macro F-score of 0.44, while a
	macro F-score of 0.62 was obtained by the same type of classifier on the binary
	classification task of distinguishing stance 'against' vaccination from stance
	'for' vaccination. These results show that vaccine stance detection in online
	forums is a difficult task, at least for the type of model investigated and for
	the relatively small training corpus that was used. Future work will therefore
	include an expansion of the training data and an evaluation of other types of
	classifiers and features.},
  url       = {http://www.aclweb.org/anthology/W17-5801}
}