A Deep Learning Artificial Neural Network Algorithm for Instance-based Arabic Language Authorship Attribution

Abstract

One of the common examples of cybercrime are identity theft and violating of intellectual property that commonly occur in social media. Authorship attribution (AA) techniques are used to extract and use several features of the text in order to identify the original author. These features are used to differentiate the writing style of one author from others. Several machine learning methods have been used to identify the AA using different languages. Few studies were conducted for Arabic AA. This paper aims to investigate the performance of deep learning-based artificial neural network (ANN) for identifying the attribution of authors using Arabic text. The applied model helps protect users in social media from identity theft and violating of their intellectual property. The experiments of this study used a dataset that includes 4,686 Arabic texts for 15 different authors. The performance of the deep learning method was compared with several machine learning methods. The experimental results showed the superior performance of deep learning for AA in Arabic language using different evaluation criteria such as F-score, accuracy, precision, and recall measures.