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In this paper, we present Arap-Tweet, which is a large-scale andmulti-dialectal corpus of Tweets from 11 regions and 16 countries in the Arabworld representing the major Arabic dialectal varieties. To build this corpus,we collected data from Twitter and we provided a team of experienced annotatorswith annotation guidelines that they used to annotate the corpus for agecategories, gender, and dialectal variety. During the data collection effort,we based our search on distinctive keywords that are specific to the differentArabic dialects and we also validated the location using Twitter API. In thispaper, we report on the corpus data collection and annotation efforts. We alsopresent some issues that we encountered during these phases. Then, we presentthe results of the evaluation performed to ensure the consistency of theannotation. The provided corpus will enrich the limited set of availablelanguage resources for Arabic and will be an invaluable enabler for developingauthor profiling tools and NLP tools for Arabic.

Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification