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