@InProceedings{lendvai-reichel:2016:ExProM,
  author    = {Lendvai, Piroska  and  Reichel, Uwe},
  title     = {Contradiction Detection for Rumorous Claims},
  booktitle = {Proceedings of the Workshop on Extra-Propositional Aspects of Meaning in Computational Linguistics (ExProM)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {31--40},
  abstract  = {The utilization of social media material in journalistic workflows is
	increasing, demanding automated methods for the identification of mis- and
	disinformation. Since textual contradiction across social media posts can be a
	signal of rumorousness, we seek to model how claims in Twitter posts are being
	textually contradicted. We identify two different contexts in which
	contradiction emerges: its broader form can be observed across independently
	posted tweets and its more specific form in threaded conversations. We define
	how the two scenarios differ in terms of central elements of argumentation:
	claims and conversation structure. We design and evaluate models for the two
	scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to
	represent claims and conversation structure implicitly in a generic inference
	model, while previous studies used explicit or no representation of these
	properties. To address noisy text, our classifiers use simple similarity
	features derived from the string and part-of-speech level. Corpus statistics
	reveal distribution differences for these features in contradictory as opposed
	to non-contradictory tweet relations, and the classifiers yield state of the
	art performance.},
  url       = {http://aclweb.org/anthology/W16-5004}
}