This article presents BOUTEF, an original and comprehensive corpus of fake news. It encompasses content in Algerian and Tunisian dialects, Modern Standard Arabic (MSA), French, and English, featuring instances of code-switching between these languages. Moreover, for the Algerian and Tunisian dialects, we have preserved both Latin and Arabic scripts in the dataset. BOUTEF comprises over 3,600 fake news posts collected from various social media platforms spanning from 2010 to 2024. This corpus is developed as part of the TRADEF 4 project and is made available to the research community. Each fake news post in BOUTEF is associated with 16 attributes, providing rich contextual information. The data was gathered from Facebook, Twitter, YouTube, and TikTok, reflecting the diverse sources of misinformation. To enhance the depth of our analysis, we introduce a novel labeling scheme consisting of 40 categories. This scheme is developed through a thorough examination of the collected corpus, and we have also retained a tagging process inspired by Claire Wardle’s categorization. BOUTEF not only contributes to the understanding of fake news in multilingual contexts but also provides valuable resources for further research in this domain.