In the context of low-resource languages, the Algerian dialect (AD) faces challenges due to the absence of annotated corpora, hindering its effective processing, notably in Machine Learning (ML) applications reliant on corpora for training and assessment. This study outlines the development process of a specialized corpus for Fake News (FN) detection and sentiment analysis (SA) in AD called FASSILA. This corpus comprises 10,087 sentences, encompassing over 19,497 unique words in AD, and addresses the significant lack of linguistic resources in the language and covers seven distinct domains. We propose an annotation scheme for FN detection and SA, detailing the data collection, cleaning, and labelling process. Remarkable Inter-Annotator Agreement indicates that the annotation scheme produces consistent annotations of high quality. Subsequent classification experiments using BERT-based models and ML models are presented, demonstrate promising results and highlight avenues for further research. The dataset is made freely available on GitHub (https://github.com/amincoding/FASSILA) to facilitate future advancements in the field.

翻译：在低资源语言的背景下，阿尔及利亚方言（AD）由于缺乏标注语料库而面临挑战，这阻碍了其有效处理，特别是在依赖语料库进行训练和评估的机器学习（ML）应用中。本研究概述了为AD中假新闻（FN）检测和情感分析（SA）而开发的专用语料库FASSILA的构建过程。该语料库包含10,087个句子，涵盖AD中超过19,497个独特词汇，解决了该语言中显著的资源匮乏问题，并覆盖了七个不同的领域。我们为FN检测和SA提出了一套标注方案，详细说明了数据收集、清洗和标注过程。显著的标注者间一致性表明，该标注方案能产生一致的高质量标注。随后使用基于BERT的模型和ML模型进行的分类实验，展示了有希望的结果，并突出了进一步研究的途径。该数据集已在GitHub（https://github.com/amincoding/FASSILA）上免费提供，以促进该领域的未来发展。