GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives
Khalid Khatib, Sara Gemelli, Saskia Heisterborg, Pritha Majumdar, Gosse Minnema, Arianna Muti, Noa Solissa
Correct Metadata for
Abstract
In this paper we report the development of our annotation methodology for the shared task FIGNEWS 2024. The objective of the shared task is to look into the layers of bias in how the war on Gaza is represented in media narrative. Our methodology follows the prescriptive paradigm, in which guidelines are detailed and refined through an iterative process in which edge cases are discussed and converged. Our IAA score (Krippendorff’s 𝛼) is 0.420, highlighting the challenging and subjective nature of the task. Our results show that 52% of posts were unbiased, 42% biased against Palestine, 5% biased against Israel, and 3% biased against both. 16% were unclear or not applicable.- Anthology ID:
- 2024.arabicnlp-1.68
- 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:
- SIGARAB
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 630–639
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.68/
- DOI:
- 10.18653/v1/2024.arabicnlp-1.68
- Bibkey:
- Cite (ACL):
- Khalid Khatib, Sara Gemelli, Saskia Heisterborg, Pritha Majumdar, Gosse Minnema, Arianna Muti, and Noa Solissa. 2024. GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 630–639, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives (Khatib et al., ArabicNLP 2024)
- Copy Citation:
- PDF:
- https://aclanthology.org/2024.arabicnlp-1.68.pdf
Export citation
@inproceedings{khatib-etal-2024-groningenannotatesgaza,
title = "{G}roningen{A}nnotates{G}aza at the {FIGNEWS} 2024 Shared Task: Analyzing Bias in Conflict Narratives",
author = "Khatib, Khalid and
Gemelli, Sara and
Heisterborg, Saskia and
Majumdar, Pritha and
Minnema, Gosse and
Muti, Arianna and
Solissa, Noa",
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-1.68/",
doi = "10.18653/v1/2024.arabicnlp-1.68",
pages = "630--639",
abstract = "In this paper we report the development of our annotation methodology for the shared task FIGNEWS 2024. The objective of the shared task is to look into the layers of bias in how the war on Gaza is represented in media narrative. Our methodology follows the prescriptive paradigm, in which guidelines are detailed and refined through an iterative process in which edge cases are discussed and converged. Our IAA score (Krippendorff`s $\alpha$) is 0.420, highlighting the challenging and subjective nature of the task. Our results show that 52{\%} of posts were unbiased, 42{\%} biased against Palestine, 5{\%} biased against Israel, and 3{\%} biased against both. 16{\%} were unclear or not applicable."
}
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<abstract>In this paper we report the development of our annotation methodology for the shared task FIGNEWS 2024. The objective of the shared task is to look into the layers of bias in how the war on Gaza is represented in media narrative. Our methodology follows the prescriptive paradigm, in which guidelines are detailed and refined through an iterative process in which edge cases are discussed and converged. Our IAA score (Krippendorff‘s α) is 0.420, highlighting the challenging and subjective nature of the task. Our results show that 52% of posts were unbiased, 42% biased against Palestine, 5% biased against Israel, and 3% biased against both. 16% were unclear or not applicable.</abstract>
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%0 Conference Proceedings %T GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives %A Khatib, Khalid %A Gemelli, Sara %A Heisterborg, Saskia %A Majumdar, Pritha %A Minnema, Gosse %A Muti, Arianna %A Solissa, Noa %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 khatib-etal-2024-groningenannotatesgaza %X In this paper we report the development of our annotation methodology for the shared task FIGNEWS 2024. The objective of the shared task is to look into the layers of bias in how the war on Gaza is represented in media narrative. Our methodology follows the prescriptive paradigm, in which guidelines are detailed and refined through an iterative process in which edge cases are discussed and converged. Our IAA score (Krippendorff‘s α) is 0.420, highlighting the challenging and subjective nature of the task. Our results show that 52% of posts were unbiased, 42% biased against Palestine, 5% biased against Israel, and 3% biased against both. 16% were unclear or not applicable. %R 10.18653/v1/2024.arabicnlp-1.68 %U https://aclanthology.org/2024.arabicnlp-1.68/ %U https://doi.org/10.18653/v1/2024.arabicnlp-1.68 %P 630-639
Markdown (Informal)
[GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives](https://aclanthology.org/2024.arabicnlp-1.68/) (Khatib et al., ArabicNLP 2024)
- GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives (Khatib et al., ArabicNLP 2024)
ACL
- Khalid Khatib, Sara Gemelli, Saskia Heisterborg, Pritha Majumdar, Gosse Minnema, Arianna Muti, and Noa Solissa. 2024. GroningenAnnotatesGaza at the FIGNEWS 2024 Shared Task: Analyzing Bias in Conflict Narratives. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 630–639, Bangkok, Thailand. Association for Computational Linguistics.