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A Deep Learning Approach for Automatic Detection of Fake News

Research Paper Published at ICON 2019, indexed in ACL Anthology by Tanik Saikh, Arkadipta De, Asif Ekbal, Pushpak Bhattacharyya

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Proceedings

16th International Conference on Natural Language Processing 2021 - https://ltrc.iiit.ac.in/icon2019/icon2019proceedings.pdf

Introduction

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 FakeNews AMT and Celebrity for fake news detection. The proposed systems yield encouraging performance, outperforming the current handcrafted feature engineering based state-of-theart 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 (i.e. model trained on FakeNews AMT and tested on Celebrity and vice versa) to explore the applicability of our systems across the domains.

Result

Dataset System Model Test Accuracy(%)
FakeNews AMT Proposed Model1 77.08
FakeNews AMT Proposed Model2 83.33
FakeNews AMT (Perez-Rosas et al. 2018) Linear SVM 74
Celebrity Proposed Model1 76.53
Celebrity Proposed Model2 79
Celebrity (Perez-Rosas et al. 2018) Linear SVM 76

Reads

Read the paper at : https://arxiv.org/abs/2005.04938

Citation

For Research Puropose cite the following:

@article{DBLP:journals/corr/abs-2005-04938,
  author    = {Tanik Saikh and
               Arkadipta De and
               Asif Ekbal and
               Pushpak Bhattacharyya},
  title     = {A Deep Learning Approach for Automatic Detection of Fake News},
  journal   = {CoRR},
  volume    = {abs/2005.04938},
  year      = {2020},
  url       = {https://arxiv.org/abs/2005.04938},
  archivePrefix = {arXiv},
  eprint    = {2005.04938},
  timestamp = {Thu, 14 May 2020 16:56:02 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2005-04938.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}