@inproceedings{abril-etal-2026-credibility,
title = "Credibility Assessment for {A}rabic News on the {G}aza War: A Hybrid Neural-Symbolic Pipeline",
author = "Abril, Sanaa and
Mouanid, Sihame and
Ben lahmar, El habib and
Zahour, Omar",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.5/",
doi = "10.63317/2hvdmcb8xkoh",
pages = "50--59",
abstract = "While misinformation has long circulated online, the Gaza conflict has intensified its visibility and spread across news websites, online portals, and social media, complicating the credibility and long-term curation of conflict-related Arabic records, including historical accounts and written testimonies. This work proposes a hybrid framework for Arabic fake news detection that combines interpretable linguistic cues with contextual semantic representations. The approach integrates fuzzy logic-based handcrafted features capturing exaggerated and sensational linguistic patterns, AraBERT contextual embeddings for semantic understanding, and a CNN-based text feature extractor for local textual patterns. These complementary features are combined into a unified representation for downstream classification. Multiple machine learning and deep learning classifiers are evaluated to identify the most effective detection model. The resulting system is deployed as a real-time web browser plugin, enabling users to automatically assess the credibility of Arabic news content during browsing"
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<abstract>While misinformation has long circulated online, the Gaza conflict has intensified its visibility and spread across news websites, online portals, and social media, complicating the credibility and long-term curation of conflict-related Arabic records, including historical accounts and written testimonies. This work proposes a hybrid framework for Arabic fake news detection that combines interpretable linguistic cues with contextual semantic representations. The approach integrates fuzzy logic-based handcrafted features capturing exaggerated and sensational linguistic patterns, AraBERT contextual embeddings for semantic understanding, and a CNN-based text feature extractor for local textual patterns. These complementary features are combined into a unified representation for downstream classification. Multiple machine learning and deep learning classifiers are evaluated to identify the most effective detection model. The resulting system is deployed as a real-time web browser plugin, enabling users to automatically assess the credibility of Arabic news content during browsing</abstract>
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<date>2026-05</date>
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%0 Conference Proceedings
%T Credibility Assessment for Arabic News on the Gaza War: A Hybrid Neural-Symbolic Pipeline
%A Abril, Sanaa
%A Mouanid, Sihame
%A Ben lahmar, El habib
%A Zahour, Omar
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F abril-etal-2026-credibility
%X While misinformation has long circulated online, the Gaza conflict has intensified its visibility and spread across news websites, online portals, and social media, complicating the credibility and long-term curation of conflict-related Arabic records, including historical accounts and written testimonies. This work proposes a hybrid framework for Arabic fake news detection that combines interpretable linguistic cues with contextual semantic representations. The approach integrates fuzzy logic-based handcrafted features capturing exaggerated and sensational linguistic patterns, AraBERT contextual embeddings for semantic understanding, and a CNN-based text feature extractor for local textual patterns. These complementary features are combined into a unified representation for downstream classification. Multiple machine learning and deep learning classifiers are evaluated to identify the most effective detection model. The resulting system is deployed as a real-time web browser plugin, enabling users to automatically assess the credibility of Arabic news content during browsing
%R 10.63317/2hvdmcb8xkoh
%U https://aclanthology.org/2026.nakbanlp-1.5/
%U https://doi.org/10.63317/2hvdmcb8xkoh
%P 50-59
Markdown (Informal)
[Credibility Assessment for Arabic News on the Gaza War: A Hybrid Neural-Symbolic Pipeline](https://aclanthology.org/2026.nakbanlp-1.5/) (Abril et al., NakbaNLP 2026)
ACL