ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Natural Language Processing
Volume 8 - 2025 | doi: 10.3389/frai.2025.1615800
This article is part of the Research TopicEmerging Techniques in Arabic Natural Language ProcessingView all 4 articles
Constructing and Evaluating ArabicStanceX: A Social Media Dataset for Arabic Stance Detection
Provisionally accepted
- 1King Abdulaziz University, Jeddah, Saudi Arabia
- 2King Saud University, Riyadh, Riyadh, Saudi Arabia
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Arabic stance detection has attracted significant interest due to the growing importance of social media in shaping public opinion. However, the lack of comprehensive datasets has limited research progress in Arabic Natural Language Processing (NLP). To address this, we introduce ArabicStanceX, a novel and extensive Arabic stance detection dataset sourced from social media, comprising 14,477 tweets across 17 diverse topics. Utilizing the transformer-based MARBERTv2 model, we explore stance detection through Multi-Topic Single Model (MTSM) strategies, achieving a promising F1 score of 0.74 for detecting 'favor' and 'against' stances, and 0.67 overall. Our experiments highlight the model's capabilities and challenges, particularly in accurately classifying neutral stances and generalizing to unseen topics. Further investigations using zero-shot and few-shot learning demonstrate the model's adaptability to new contexts. This study significantly advances Arabic NLP, providing crucial resources and insights into stance detection methodologies and future research directions. The dataset is publicly available. 1
Keywords: stance detection, Arabic language, opinion mining, social media analysis, Arabic NLP
Received: 21 Apr 2025; Accepted: 26 May 2025.
Copyright: © 2025 Alkhathlan, Alahmadi, Kateb and Al-Khalifa. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ali Alkhathlan, King Abdulaziz University, Jeddah, Saudi Arabia
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