@inproceedings{de-sarkar-etal-2018-attending,
title = "Attending Sentences to detect Satirical Fake News",
author = "De Sarkar, Sohan and
Yang, Fan and
Mukherjee, Arjun",
editor = "Bender, Emily M. and
Derczynski, Leon and
Isabelle, Pierre",
booktitle = "Proceedings of the 27th International Conference on Computational Linguistics",
month = aug,
year = "2018",
address = "Santa Fe, New Mexico, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/C18-1285",
pages = "3371--3380",
abstract = "Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.",
}
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<abstract>Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.</abstract>
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%0 Conference Proceedings
%T Attending Sentences to detect Satirical Fake News
%A De Sarkar, Sohan
%A Yang, Fan
%A Mukherjee, Arjun
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F de-sarkar-etal-2018-attending
%X Satirical news detection is important in order to prevent the spread of misinformation over the Internet. Existing approaches to capture news satire use machine learning models such as SVM and hierarchical neural networks along with hand-engineered features, but do not explore sentence and document difference. This paper proposes a robust, hierarchical deep neural network approach for satire detection, which is capable of capturing satire both at the sentence level and at the document level. The architecture incorporates pluggable generic neural networks like CNN, GRU, and LSTM. Experimental results on real world news satire dataset show substantial performance gains demonstrating the effectiveness of our proposed approach. An inspection of the learned models reveals the existence of key sentences that control the presence of satire in news.
%U https://aclanthology.org/C18-1285
%P 3371-3380
Markdown (Informal)
[Attending Sentences to detect Satirical Fake News](https://aclanthology.org/C18-1285) (De Sarkar et al., COLING 2018)
ACL
- Sohan De Sarkar, Fan Yang, and Arjun Mukherjee. 2018. Attending Sentences to detect Satirical Fake News. In Proceedings of the 27th International Conference on Computational Linguistics, pages 3371–3380, Santa Fe, New Mexico, USA. Association for Computational Linguistics.