@inproceedings{agrawal-etal-2022-towards,
title = "Towards Detecting Political Bias in {H}indi News Articles",
author = "Agrawal, Samyak and
Gupta, Kshitij and
Gautam, Devansh and
Mamidi, Radhika",
editor = "Louvan, Samuel and
Madotto, Andrea and
Madureira, Brielen",
booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.acl-srw.17/",
doi = "10.18653/v1/2022.acl-srw.17",
pages = "239--244",
abstract = "Political propaganda in recent times has been amplified by media news portals through biased reporting, creating untruthful narratives on serious issues causing misinformed public opinions with interests of siding and helping a particular political party. This issue proposes a challenging NLP task of detecting political bias in news articles. We propose a transformer-based transfer learning method to fine-tune the pre-trained network on our data for this bias detection. As the required dataset for this particular task was not available, we created our dataset comprising 1388 Hindi news articles and their headlines from various Hindi news media outlets. We marked them on whether they are biased towards, against, or neutral to BJP, a political party, and the current ruling party at the centre in India."
}
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<abstract>Political propaganda in recent times has been amplified by media news portals through biased reporting, creating untruthful narratives on serious issues causing misinformed public opinions with interests of siding and helping a particular political party. This issue proposes a challenging NLP task of detecting political bias in news articles. We propose a transformer-based transfer learning method to fine-tune the pre-trained network on our data for this bias detection. As the required dataset for this particular task was not available, we created our dataset comprising 1388 Hindi news articles and their headlines from various Hindi news media outlets. We marked them on whether they are biased towards, against, or neutral to BJP, a political party, and the current ruling party at the centre in India.</abstract>
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%0 Conference Proceedings
%T Towards Detecting Political Bias in Hindi News Articles
%A Agrawal, Samyak
%A Gupta, Kshitij
%A Gautam, Devansh
%A Mamidi, Radhika
%Y Louvan, Samuel
%Y Madotto, Andrea
%Y Madureira, Brielen
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F agrawal-etal-2022-towards
%X Political propaganda in recent times has been amplified by media news portals through biased reporting, creating untruthful narratives on serious issues causing misinformed public opinions with interests of siding and helping a particular political party. This issue proposes a challenging NLP task of detecting political bias in news articles. We propose a transformer-based transfer learning method to fine-tune the pre-trained network on our data for this bias detection. As the required dataset for this particular task was not available, we created our dataset comprising 1388 Hindi news articles and their headlines from various Hindi news media outlets. We marked them on whether they are biased towards, against, or neutral to BJP, a political party, and the current ruling party at the centre in India.
%R 10.18653/v1/2022.acl-srw.17
%U https://aclanthology.org/2022.acl-srw.17/
%U https://doi.org/10.18653/v1/2022.acl-srw.17
%P 239-244
Markdown (Informal)
[Towards Detecting Political Bias in Hindi News Articles](https://aclanthology.org/2022.acl-srw.17/) (Agrawal et al., ACL 2022)
ACL
- Samyak Agrawal, Kshitij Gupta, Devansh Gautam, and Radhika Mamidi. 2022. Towards Detecting Political Bias in Hindi News Articles. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop, pages 239–244, Dublin, Ireland. Association for Computational Linguistics.