@InProceedings{long-EtAl:2017:I17-2,
  author    = {Long, Yunfei  and  Lu, Qin  and  Xiang, Rong  and  Li, Minglei  and  Huang, Chu-Ren},
  title     = {Fake News Detection Through Multi-Perspective Speaker Profiles},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {252--256},
  abstract  = {Automatic fake news detection is an important, yet very challenging topic.
	Traditional methods using lexical features have only very limited success. This
	paper proposes a novel method to incorporate speaker profiles into an attention
	based LSTM model for fake news detection. Speaker profiles contribute to the
	model in two ways. One is to include them in the attention model. The other
	includes them as additional input data. By adding speaker profiles such as
	party affiliation, speaker title, location and credit history, our model
	outperforms the state-of-the-art method by 14.5% in accuracy using a benchmark
	fake news detection dataset. This proves that speaker profiles provide valuable
	information to validate the credibility of news articles.
	Author{4}{Affiliation}},
  url       = {http://www.aclweb.org/anthology/I17-2043}
}