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.

本研究针对阿尔及利亚方言（AD）在假新闻检测和情感分析方面缺乏注释语料库的问题，开发了FASSILA语料库，包含10,087个句子和19,497个独特词汇。通过详细的数据收集和标注过程，该研究提出了一种高质量的标注方案，并通过BERT和机器学习模型的分类实验展示了良好的效果，推动了该领域的进一步研究。

FASSILA：阿尔及利亚方言假新闻检测与情感分析语料库