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    <abstract>We present ArabMisinfo, a corpus of 274,837 public Facebook Page posts in Arabic spanning 2014-2024 and focused on news and public affairs. Each record includes page-level metadata, UTC timestamps, link domains, and platform-native engagement signals (likes, comments, shares, and the seven post-2016 reaction types). We accompany the corpus with reproducible diagnostics for engagement and polarization, the Polarization Index (PI), Share-to-Like Anomaly (SLA), Risk Triage Index (RTI), and Engagement Anomaly Rule (EPR). It provides scripts that deterministically reproduce all tables and figures. To address the absence of content-level labels, we contribute a discourse role annotation of 10,000 posts (3.6% of the corpus) using Fanar-C-2-27B, classifying each post into one of six roles. The annotation reveals that Arabic Facebook misinformation discourse is dominated by counter-information activity: 30.6% of posts are Rumor Denial or Official Correction, outpacing Unverified Claim posts (12.3%) by a 2.5:1 ratio, while 19.3% represent incidental keyword matches, directly quantifying the sampling noise introduced by keyword-based collection. To comply with platform terms and copyright, we publicly release Post IDs, Page IDs, and full metadata and derived indices; message text is available only under a research data-use agreement (DUA). ArabMisinfo enables engagement-aware analyses of Arabic misinformation ecosystems and, to our knowledge, provides the one of the first discourse-role-annotated Arabic Facebook corpora at this scale.</abstract>
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%0 Conference Proceedings
%T ArabMisinfo: A Decade-Long Arabic Facebook Corpus for Misinformation Discourse and Engagement Analysis
%A Zaghouani, Wajdi
%A Ibrahim, Shimaa
%A Bessghaier, Mabrouka
%A Biswas, Md. Rafiul
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F zaghouani-etal-2026-arabmisinfo
%X We present ArabMisinfo, a corpus of 274,837 public Facebook Page posts in Arabic spanning 2014-2024 and focused on news and public affairs. Each record includes page-level metadata, UTC timestamps, link domains, and platform-native engagement signals (likes, comments, shares, and the seven post-2016 reaction types). We accompany the corpus with reproducible diagnostics for engagement and polarization, the Polarization Index (PI), Share-to-Like Anomaly (SLA), Risk Triage Index (RTI), and Engagement Anomaly Rule (EPR). It provides scripts that deterministically reproduce all tables and figures. To address the absence of content-level labels, we contribute a discourse role annotation of 10,000 posts (3.6% of the corpus) using Fanar-C-2-27B, classifying each post into one of six roles. The annotation reveals that Arabic Facebook misinformation discourse is dominated by counter-information activity: 30.6% of posts are Rumor Denial or Official Correction, outpacing Unverified Claim posts (12.3%) by a 2.5:1 ratio, while 19.3% represent incidental keyword matches, directly quantifying the sampling noise introduced by keyword-based collection. To comply with platform terms and copyright, we publicly release Post IDs, Page IDs, and full metadata and derived indices; message text is available only under a research data-use agreement (DUA). ArabMisinfo enables engagement-aware analyses of Arabic misinformation ecosystems and, to our knowledge, provides the one of the first discourse-role-annotated Arabic Facebook corpora at this scale.
%U https://aclanthology.org/2026.findings-aacl.192/
%P 3809-3820

Markdown (Informal)

[ArabMisinfo: A Decade-Long Arabic Facebook Corpus for Misinformation Discourse and Engagement Analysis](https://aclanthology.org/2026.findings-aacl.192/) (Zaghouani et al., Findings 2026)

• ArabMisinfo: A Decade-Long Arabic Facebook Corpus for Misinformation Discourse and Engagement Analysis (Zaghouani et al., Findings 2026)

ACL

• Wajdi Zaghouani, Shimaa Ibrahim, Mabrouka Bessghaier, and Md. Rafiul Biswas. 2026. ArabMisinfo: A Decade-Long Arabic Facebook Corpus for Misinformation Discourse and Engagement Analysis. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 3809–3820, Hengqin, China. Association for Computational Linguistics.