Stance detection in Arabic with a multi-dialectal cross-domain stance corpus

• Published in: Social Network Analysis and Mining Vol. 14; no. 1; p. 161
• Main Authors: Charfi, Anis, Bessghaier, Mabrouka, Atalla, Andria, Akasheh, Raghda, Al-Emadi, Sara, Zaghouani, Wajdi
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 16.08.2024; Springer Nature B.V
• Subjects: Accuracy; Applications of Graph Theory and Complex Networks; Arabic language; Carbonated beverages; Computer Science; Corpus linguistics; Data collection; Data Mining and Knowledge Discovery; Datasets; Dialects; Economics; Game Theory; Humanities; Law; Machine learning; Methodology of the Social Sciences; Original Article; Patient satisfaction; Product reviews; Regional dialects; Research methodology; Sentences; Sentiment analysis; Social and Behav. Sciences; Social networks; Soft drinks; Standard dialects; Statistics for Social Sciences; Topics; Egypt; Polarization; Arabic; Stance detection; Machine learning; Natural language processing; Arabic dialects; Corpus

We present a cross-domain and multi-dialectal stance corpus for Arabic, covering the major dialect groups and four Arab regions. This research provides an important language resource for automating the task of stance detection in Dialectal Arabic while carefully considering the subtle differences in...