@InProceedings{kochkina-liakata-augenstein:2017:SemEval,
  author    = {Kochkina, Elena  and  Liakata, Maria  and  Augenstein, Isabelle},
  title     = {Turing at SemEval-2017 Task 8: Sequential Approach to Rumour Stance Classification with Branch-LSTM},
  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     = {475--480},
  abstract  = {This paper describes team Turing's submission to SemEval 2017 RumourEval:
	Determining rumour veracity and support for rumours (SemEval 2017 Task 8,
	Subtask A). Subtask A addresses the challenge of rumour stance classification,
	which involves identifying the attitude of Twitter users towards the
	truthfulness of the rumour they are discussing. Stance classification is
	considered to be an important step towards rumour verification, therefore
	performing well in this task is expected to be useful in debunking false
	rumours. In this work we classify a set of Twitter posts discussing rumours
	into either supporting, denying, questioning or commenting on the underlying
	rumours. We propose a LSTM-based sequential model that, through modelling the
	conversational structure of tweets, which achieves an accuracy of 0.784 on the
	RumourEval test set outperforming all other systems in Subtask A.},
  url       = {http://www.aclweb.org/anthology/S17-2083}
}