@InProceedings{reichel-lendvai:2016:WNUT,
  author    = {Reichel, Uwe  and  Lendvai, Piroska},
  title     = {Veracity Computing from Lexical Cues and Perceived Certainty Trends},
  booktitle = {Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {33--42},
  abstract  = {We present a data-driven method for determining the veracity of a set of
	rumorous claims on social media data. Tweets from different sources pertaining
	to a rumor are processed on three levels: first, factuality values are assigned
	to each tweet based on four textual cue categories relevant for our journalism
	use case; these amalgamate speaker support in terms of polarity and commitment
	in terms of certainty and speculation. Next, the proportions of these lexical
	cues are utilized as predictors for tweet certainty in a generalized linear
	regression model. Subsequently, lexical cue proportions, predicted certainty,
	as well as their time course characteristics are used to compute veracity for
	each rumor in terms of the identity of the rumor-resolving tweet and its binary
	resolution value judgment. The system operates without access to
	extralinguistic resources. Evaluated on the data portion for which hand-labeled
	examples were available, it achieves .74 F1-score on identifying rumor
	resolving tweets and .76 F1-score on predicting if a rumor is resolved as true
	or false.},
  url       = {http://aclweb.org/anthology/W16-3907}
}