针对阿拉伯语方言谣言检测数据少难题，提出端到端混合框架。
An End-to-End Hybrid Framework for Rumour Detection in Low-Resources Algerian Dialect
- 结合真实与合成数据构建方言标注集，自动相似度标注。
- 混合模型用Transformer嵌入+传统分类器，F1达0.84最优。
- 领域预训练比模型大小更重要，适合低资源方言场景。
社交媒体的快速发展加剧了谣言传播。在阿尔及利亚语境下，这一问题尤为严峻，因方言内容非正式且混用多种语言，标注资源稀缺，标准阿拉伯语NLP工具对方言文本效果有限。本文提出一种面向阿尔及利亚方言社交媒体内容的端到端谣言检测混合框架。通过整合真实社交帖子、合成数据和FASSILA语料库，采用基于相似度的自动标注流程构建领域特定标注数据集。引入音译管道生成阿拉伯字母与Arabizi双语并行数据集。评估了多种方法，包括传统机器学习、深度学习、Transformer及混合模型。实验表明，将Transformer嵌入与传统分类器结合的混合方法表现最佳，F1分数达0.84。同时发现，领域特定预训练比模型规模更重要，社交媒体训练模型优于在正式阿拉伯语语料上训练的大模型。结果证明了在低资源阿尔及利亚方言环境下谣言检测的可行性。
原文摘要 · Abstract (English)
The rapid growth of social media has intensified the spread of rumours. This issue is more challenging in the Algerian context due to the informal and code-switched nature of dialectal content, the scarcity of annotated resources, and the limited effectiveness of standard Arabic NLP tools on dialect text. This paper presents an end-to-end rumour detection hybrid framework for Algerian dialect social media content. We build a domain-specific annotated dataset by combining real social media posts, synthetic data, and the FASSILA corpus, with automatic labeling based on a similarity-based annotation process. A transliteration pipeline is also introduced to generate parallel datasets in Arabic script and Arabizi. We evaluate multiple approaches, including classical machine learning, deep learning, transformers, and hybrid models. Experimental results show that a hybrid approach combining transformer embeddings with a classical classifier achieves the best performance, reaching an F1-score of 0.84. We also find that domain-specific pre-training is more important than model size, with social media-trained models outperforming larger models trained on formal Arabic corpora. These results demonstrate the feasibility of rumour detection in low-resource Algerian dialect settings.
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