@InProceedings{zubiaga-EtAl:2016:COLING,
  author    = {Zubiaga, Arkaitz  and  Kochkina, Elena  and  Liakata, Maria  and  Procter, Rob  and  Lukasik, Michal},
  title     = {Stance Classification in Rumours as a Sequential Task Exploiting the Tree Structure of Social Media Conversations},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2438--2448},
  abstract  = {Rumour stance classification, the task that determines if each tweet in a
	collection discussing a rumour is supporting, denying, questioning or simply
	commenting on the rumour, has been attracting substantial interest. Here we
	introduce a novel approach that makes use of the sequence of transitions
	observed in tree-structured conversation threads in Twitter. The conversation
	threads are formed by harvesting users' replies to one another, which results
	in a nested tree-like structure. Previous work addressing the stance
	classification task has treated each tweet as a separate unit. Here we analyse
	tweets by virtue of their position in a sequence and test two sequential
	classifiers, Linear-Chain CRF and Tree CRF, each of which makes different
	assumptions about the conversational structure. We experiment with eight
	Twitter datasets, collected during breaking news, and show that exploiting the
	sequential structure of Twitter conversations achieves significant improvements
	over the non-sequential methods. Our work is the first to model Twitter
	conversations as a tree structure in this manner, introducing a novel way of
	tackling NLP tasks on Twitter conversations.},
  url       = {http://aclweb.org/anthology/C16-1230}
}