Corpora are important assets within the natural language processing (NLP) and linguistics communities, as they allow the training of models and corpus-based studies of languages. However, corpora do not exist for many languages and language varieties, such as Acadian French. In this paper, we first show that off-the-shelf NLP systems perform more poorly on Acadian French than on standard French. An Acadian French corpus could, therefore, potentially be used to improve NLP models for this dialect. Then, leveraging web-as-corpus methodologies, specifically BootCaT, domain crawling, and social media scraping, we create three corpora of Acadian French. To evaluate these corpora, drawing on the linguistic literature on Acadian French, we propose 22 statistical corpus-based measures of the extent to which a corpus is Acadian French. We use these measures to compare these newly built corpora to known Acadian French text and find that all three corpora include some traces of Acadian French.