Author profiling is the process of finding
characteristics that make up an author’s profile. This paper
presents a machine learning-based author profiling model for
Arabic users, considering the author’s regional dialect as a
crucial characteristic. Various classification algorithms have
been implemented: decision tree, KNN, multilayer perceptron,
random forest, and support vector machines. A pair of
Continuous Bag-of-Word (CBOW) models has been used for
word representation. A well-known data set has been used to
evaluate the proposed model and a data augmentation process
has been implemented to improve the quality of training data.
Support vector machines achieved a 50.52% f1-score,
outperforming other models.