@InProceedings{nakov-EtAl:2017:RANLP,
  author    = {Nakov, Preslav  and  Mihaylova, Tsvetomila  and  M\`{a}rquez, Llu\'{i}s  and  Shiroya, Yashkumar  and  Koychev, Ivan},
  title     = {Do Not Trust the Trolls: Predicting Credibility in Community Question Answering Forums},
  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     = {551--560},
  abstract  = {We address information credibility in community forums, in a setting in which
	the credibility of an answer posted in a question thread by a particular user
	has to be predicted. First, we motivate the problem and we create a publicly
	available annotated English corpus by crowdsourcing. Second, we propose a large
	set of features to predict the credibility of the answers. The features model
	the user, the answer, the question, the thread as a whole, and the interaction
	between them. Our experiments with ranking SVMs show that the credibility
	labels can be predicted with high performance according to several standard IR
	ranking metrics, thus supporting the potential usage of this layer of
	credibility information in practical applications. The features modeling the
	profile of the user (in particular trollness) turn out to be most important,
	but embedding features modeling the answer and the similarity between the
	question and the answer are also very relevant. Overall, half of the gap
	between the baseline performance and the perfect classifier can be covered
	using the proposed features.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_072}
}