  This study aims at investigating the effect of applying single learner
machine learning approach and ensemble machine learning approach for offensive
language detection on Arabic language. Classifying Arabic social media text is
a very challenging task due to the ambiguity and informality of the written
format of the text. Arabic language has multiple dialects with diverse
vocabularies and structures, which increase the complexity of obtaining high
classification performance. Our study shows significant impact for applying
ensemble machine learning approach over the single learner machine learning
approach. Among the trained ensemble machine learning classifiers, bagging
performs the best in offensive language detection with F1 score of 88%, which
exceeds the score obtained by the best single learner classifier by 6%. Our
findings highlight the great opportunities of investing more efforts in
promoting the ensemble machine learning approach solutions for offensive
language detection models.
