NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media
Sadaf Abdul Rauf, Huda Sarfraz, Saadia Nauman, Arooj Fatima, SadafZiafat SadafZiafat, Momina Ishfaq, Alishba Suboor, Hammad Afzal, Seemab Latif
Abstract
In this paper, we present our methodology and findings from participating in the FIGNEWS 2024 shared task on annotating news fragments on the Gaza-Israel war for bias and propaganda detection. The task aimed to refine the FIGNEWS 2024 annotation guidelines and to contribute to the creation of a comprehensive dataset to advance research in this field. Our team employed a multi-faceted approach to ensure high accuracy in data annotations. Our results highlight key challenges in detecting bias and propaganda, such as the need for more comprehensive guidelines. Our team ranked first in all tracks for propaganda annotation. For Bias, the team stood in first place for the Guidelines and IAA tracks, and in second place for the Quantity and Consistency tracks.- Anthology ID:
- 2024.arabicnlp-1.61
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 573–579
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.61
- DOI:
- 10.18653/v1/2024.arabicnlp-1.61
- Bibkey:
- Cite (ACL):
- Sadaf Abdul Rauf, Huda Sarfraz, Saadia Nauman, Arooj Fatima, SadafZiafat SadafZiafat, Momina Ishfaq, Alishba Suboor, Hammad Afzal, and Seemab Latif. 2024. NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 573–579, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media (Abdul Rauf et al., ArabicNLP-WS 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.61.pdf
Export citation
@inproceedings{abdul-rauf-etal-2024-nlpcolab,
title = "{NLPC}olab at {F}ig{N}ews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media",
author = "Abdul Rauf, Sadaf and
Sarfraz, Huda and
Nauman, Saadia and
Fatima, Arooj and
SadafZiafat, SadafZiafat and
Ishfaq, Momina and
Suboor, Alishba and
Afzal, Hammad and
Latif, Seemab",
editor = "Habash, Nizar and
Bouamor, Houda and
Eskander, Ramy and
Tomeh, Nadi and
Abu Farha, Ibrahim and
Abdelali, Ahmed and
Touileb, Samia and
Hamed, Injy and
Onaizan, Yaser and
Alhafni, Bashar and
Antoun, Wissam and
Khalifa, Salam and
Haddad, Hatem and
Zitouni, Imed and
AlKhamissi, Badr and
Almatham, Rawan and
Mrini, Khalil",
booktitle = "Proceedings of The Second Arabic Natural Language Processing Conference",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.arabicnlp-1.61",
doi = "10.18653/v1/2024.arabicnlp-1.61",
pages = "573--579",
abstract = "In this paper, we present our methodology and findings from participating in the FIGNEWS 2024 shared task on annotating news fragments on the Gaza-Israel war for bias and propaganda detection. The task aimed to refine the FIGNEWS 2024 annotation guidelines and to contribute to the creation of a comprehensive dataset to advance research in this field. Our team employed a multi-faceted approach to ensure high accuracy in data annotations. Our results highlight key challenges in detecting bias and propaganda, such as the need for more comprehensive guidelines. Our team ranked first in all tracks for propaganda annotation. For Bias, the team stood in first place for the Guidelines and IAA tracks, and in second place for the Quantity and Consistency tracks.",
}
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<abstract>In this paper, we present our methodology and findings from participating in the FIGNEWS 2024 shared task on annotating news fragments on the Gaza-Israel war for bias and propaganda detection. The task aimed to refine the FIGNEWS 2024 annotation guidelines and to contribute to the creation of a comprehensive dataset to advance research in this field. Our team employed a multi-faceted approach to ensure high accuracy in data annotations. Our results highlight key challenges in detecting bias and propaganda, such as the need for more comprehensive guidelines. Our team ranked first in all tracks for propaganda annotation. For Bias, the team stood in first place for the Guidelines and IAA tracks, and in second place for the Quantity and Consistency tracks.</abstract>
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%0 Conference Proceedings %T NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media %A Abdul Rauf, Sadaf %A Sarfraz, Huda %A Nauman, Saadia %A Fatima, Arooj %A SadafZiafat, SadafZiafat %A Ishfaq, Momina %A Suboor, Alishba %A Afzal, Hammad %A Latif, Seemab %Y Habash, Nizar %Y Bouamor, Houda %Y Eskander, Ramy %Y Tomeh, Nadi %Y Abu Farha, Ibrahim %Y Abdelali, Ahmed %Y Touileb, Samia %Y Hamed, Injy %Y Onaizan, Yaser %Y Alhafni, Bashar %Y Antoun, Wissam %Y Khalifa, Salam %Y Haddad, Hatem %Y Zitouni, Imed %Y AlKhamissi, Badr %Y Almatham, Rawan %Y Mrini, Khalil %S Proceedings of The Second Arabic Natural Language Processing Conference %D 2024 %8 August %I Association for Computational Linguistics %C Bangkok, Thailand %F abdul-rauf-etal-2024-nlpcolab %X In this paper, we present our methodology and findings from participating in the FIGNEWS 2024 shared task on annotating news fragments on the Gaza-Israel war for bias and propaganda detection. The task aimed to refine the FIGNEWS 2024 annotation guidelines and to contribute to the creation of a comprehensive dataset to advance research in this field. Our team employed a multi-faceted approach to ensure high accuracy in data annotations. Our results highlight key challenges in detecting bias and propaganda, such as the need for more comprehensive guidelines. Our team ranked first in all tracks for propaganda annotation. For Bias, the team stood in first place for the Guidelines and IAA tracks, and in second place for the Quantity and Consistency tracks. %R 10.18653/v1/2024.arabicnlp-1.61 %U https://aclanthology.org/2024.arabicnlp-1.61 %U https://doi.org/10.18653/v1/2024.arabicnlp-1.61 %P 573-579
Markdown (Informal)
[NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media](https://aclanthology.org/2024.arabicnlp-1.61) (Abdul Rauf et al., ArabicNLP-WS 2024)
- NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media (Abdul Rauf et al., ArabicNLP-WS 2024)
ACL
- Sadaf Abdul Rauf, Huda Sarfraz, Saadia Nauman, Arooj Fatima, SadafZiafat SadafZiafat, Momina Ishfaq, Alishba Suboor, Hammad Afzal, and Seemab Latif. 2024. NLPColab at FigNews 2024 Shared Task: Challenges in Bias and Propaganda Annotation for News Media. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 573–579, Bangkok, Thailand. Association for Computational Linguistics.