@inproceedings{nabhani-etal-2025-integrating,
title = "Integrating Argumentation Features for Enhanced Propaganda Detection in {A}rabic Narratives on the Israeli War on {G}aza",
author = "Nabhani, Sara and
Borg, Claudia and
Al Khatib, Khalid and
Micallef, Kurt",
editor = "Jarrar, Mustafa and
Habash, Habash and
El-Haj, Mo",
booktitle = "Proceedings of the first International Workshop on Nakba Narratives as Language Resources",
month = jan,
year = "2025",
address = "Abu Dhabi",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.nakbanlp-1.14/",
pages = "127--149",
abstract = "Propaganda significantly shapes public opinion, especially in conflict-driven contexts like the Israeli-Palestinian conflict. This study explores the integration of argumentation features, such as claims, premises, and major claims, into machine learning models to enhance the detection of propaganda techniques in Arabic media. By leveraging datasets annotated with fine-grained propaganda techniques and employing crosslingual and multilingual NLP methods, along with GPT-4-based annotations, we demonstrate consistent performance improvements. A qualitative analysis of Arabic media narratives on the Israeli war on Gaza further reveals the model`s capability to identify diverse rhetorical strategies, offering insights into the dynamics of propaganda. These findings emphasize the potential of combining NLP with argumentation features to foster transparency and informed discourse in politically charged settings."
}
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<abstract>Propaganda significantly shapes public opinion, especially in conflict-driven contexts like the Israeli-Palestinian conflict. This study explores the integration of argumentation features, such as claims, premises, and major claims, into machine learning models to enhance the detection of propaganda techniques in Arabic media. By leveraging datasets annotated with fine-grained propaganda techniques and employing crosslingual and multilingual NLP methods, along with GPT-4-based annotations, we demonstrate consistent performance improvements. A qualitative analysis of Arabic media narratives on the Israeli war on Gaza further reveals the model‘s capability to identify diverse rhetorical strategies, offering insights into the dynamics of propaganda. These findings emphasize the potential of combining NLP with argumentation features to foster transparency and informed discourse in politically charged settings.</abstract>
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%0 Conference Proceedings
%T Integrating Argumentation Features for Enhanced Propaganda Detection in Arabic Narratives on the Israeli War on Gaza
%A Nabhani, Sara
%A Borg, Claudia
%A Al Khatib, Khalid
%A Micallef, Kurt
%Y Jarrar, Mustafa
%Y Habash, Habash
%Y El-Haj, Mo
%S Proceedings of the first International Workshop on Nakba Narratives as Language Resources
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F nabhani-etal-2025-integrating
%X Propaganda significantly shapes public opinion, especially in conflict-driven contexts like the Israeli-Palestinian conflict. This study explores the integration of argumentation features, such as claims, premises, and major claims, into machine learning models to enhance the detection of propaganda techniques in Arabic media. By leveraging datasets annotated with fine-grained propaganda techniques and employing crosslingual and multilingual NLP methods, along with GPT-4-based annotations, we demonstrate consistent performance improvements. A qualitative analysis of Arabic media narratives on the Israeli war on Gaza further reveals the model‘s capability to identify diverse rhetorical strategies, offering insights into the dynamics of propaganda. These findings emphasize the potential of combining NLP with argumentation features to foster transparency and informed discourse in politically charged settings.
%U https://aclanthology.org/2025.nakbanlp-1.14/
%P 127-149
Markdown (Informal)
[Integrating Argumentation Features for Enhanced Propaganda Detection in Arabic Narratives on the Israeli War on Gaza](https://aclanthology.org/2025.nakbanlp-1.14/) (Nabhani et al., NakbaNLP 2025)
ACL