@InProceedings{yang-mukherjee-dragut:2017:EMNLP2017,
  author    = {Yang, Fan  and  Mukherjee, Arjun  and  Dragut, Eduard},
  title     = {Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {1979--1989},
  abstract  = {Satirical news is considered to be entertainment, but it is potentially
	deceptive and harmful. Despite the embedded genre in the article, not everyone
	can recognize the satirical cues and therefore believe the news as true news.
	We observe that satirical cues are often reflected in certain paragraphs rather
	than the whole document. Existing works only consider document-level features
	to detect the satire, which could be limited. We consider paragraph-level
	linguistic features to unveil the satire by incorporating neural network and
	attention mechanism. We investigate the difference between paragraph-level
	features and document-level features, and analyze them on a large satirical
	news dataset. The evaluation shows that the proposed model detects satirical
	news effectively and reveals what features are important at which level.},
  url       = {https://www.aclweb.org/anthology/D17-1211}
}