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Behavior analysis in Arabic social media

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Abstract

Social media has allowed all individuals, organizations, and businesses to share their opinions, ideas, and inclinations with others. These opinions might be negative or positive; however, they affect society in a notable way, and this is a formidable challenge especially in Iraqi society, which is a mixture of different cultures and trends. Therefore, this paper utilizes a new model with the adopted classifier "Identity classifier", which is used to analyze the behavior of Facebook posts/comments in the Iraqi-Arabic dialect and classify them into two categories: wicked and non-wicked. The accuracy results of this model reached 85.4%. Moreover, comparisons of the proposed classifier with the most common classifiers have been conducted using the same data to evaluate the suggested classifier. It was observed that this classifier outperformed other classifiers with regard to accuracy when compared to previous studies. We note this is a very positive result.

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Acknowledgements

This work is supported by the University of Kerbala.

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Authors and Affiliations

  1. Department of Computer Science, University of Kerbala, Karbala, Iraq

    Zinah Abdulridha Abutiheen, Enas Ali Mohammed & Mohsin Hasan Hussein

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  1. Zinah Abdulridha Abutiheen
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  2. Enas Ali Mohammed
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  3. Mohsin Hasan Hussein
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Correspondence to Zinah Abdulridha Abutiheen.

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Abutiheen, Z.A., Mohammed, E.A. & Hussein, M.H. Behavior analysis in Arabic social media. Int J Speech Technol 25, 659–666 (2022). https://doi.org/10.1007/s10772-021-09856-6

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