@inproceedings{saikh-etal-2019-deep,
title = "A Deep Learning Approach for Automatic Detection of Fake News",
author = "Saikh, Tanik and
De, Arkadipta and
Ekbal, Asif and
Bhattacharyya, Pushpak",
editor = "Sharma, Dipti Misra and
Bhattacharya, Pushpak",
booktitle = "Proceedings of the 16th International Conference on Natural Language Processing",
month = dec,
year = "2019",
address = "International Institute of Information Technology, Hyderabad, India",
publisher = "NLP Association of India",
url = "https://aclanthology.org/2019.icon-1.27",
pages = "230--238",
abstract = "Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely Fake News AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current hand-crafted feature engineering based state-of-the-art system with a significant margin of 3.08{\%} and 9.3{\%} by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.",
}
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<abstract>Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely Fake News AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current hand-crafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.</abstract>
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%0 Conference Proceedings
%T A Deep Learning Approach for Automatic Detection of Fake News
%A Saikh, Tanik
%A De, Arkadipta
%A Ekbal, Asif
%A Bhattacharyya, Pushpak
%Y Sharma, Dipti Misra
%Y Bhattacharya, Pushpak
%S Proceedings of the 16th International Conference on Natural Language Processing
%D 2019
%8 December
%I NLP Association of India
%C International Institute of Information Technology, Hyderabad, India
%F saikh-etal-2019-deep
%X Fake news detection is a very prominent and essential task in the field of journalism. This challenging problem is seen so far in the field of politics, but it could be even more challenging when it is to be determined in the multi-domain platform. In this paper, we propose two effective models based on deep learning for solving fake news detection problem in online news contents of multiple domains. We evaluate our techniques on the two recently released datasets, namely Fake News AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current hand-crafted feature engineering based state-of-the-art system with a significant margin of 3.08% and 9.3% by the two models, respectively. In order to exploit the datasets, available for the related tasks, we perform cross-domain analysis (model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.
%U https://aclanthology.org/2019.icon-1.27
%P 230-238
Markdown (Informal)
[A Deep Learning Approach for Automatic Detection of Fake News](https://aclanthology.org/2019.icon-1.27) (Saikh et al., ICON 2019)
ACL
- Tanik Saikh, Arkadipta De, Asif Ekbal, and Pushpak Bhattacharyya. 2019. A Deep Learning Approach for Automatic Detection of Fake News. In Proceedings of the 16th International Conference on Natural Language Processing, pages 230–238, International Institute of Information Technology, Hyderabad, India. NLP Association of India.