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Arabic spam tweets classification using deep learning

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Abstract

With the increased use of social network sites, such as Twitter, attackers exploit these platforms to spread counterfeit content. Such content can be fake advertisements or illegal content. Classifying such content is a challenging task, especially in Arabic. The Arabic language has a complex structure and makes classification tasks more difficult. This paper presents an approach to classifying Arabic tweets using classical machine learning (non-deep machine learning) and deep learning techniques. Tweets corpus were collected through Twitter API and labelled manually to get a reliable dataset. For an efficient classifier, feature extraction is applied to the corpus dataset. Then, two learning techniques are used for each feature extraction technique on the created dataset using N-gram models (uni-gram, bi-gram, and char-gram). The applied classical machine learning algorithms are support vector machines, neural networks, logistics regression, and naïve Bayes. Global vector (GloVe) and fastText learning models are utilised for the deep learning approaches. The Precision, Recall, and F1-score are the suggested performance measures calculated in this paper. Afterwards, the dataset is increased using the synthetic minority oversampling technique class to create a balanced dataset. After applying the classical machine learning models, the experimental results show that the neural network algorithm outperforms the other algorithms. Moreover, the GloVe outperforms the fastText model for the deep learning approach.

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Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Funding

This work was funded by Zayed University—Start-up research grant [Grant Number R20081].

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Authors and Affiliations

  1. Department of Computing and Applied Technology, College of Technological Innovation, Zayed University, Abu Dhabi, UAE

    Sanaa Kaddoura & Safaa Henno

  2. Department of Information Technology, St. Xavier’s Catholic College of Engineering, Nagercoil, India

    Suja A. Alex

  3. Computing Department, Academic Development Division, Sabis Educational Services, Choueifat, Lebanon

    Maher Itani

  4. Department of Data Science, King Hussein School of Computing Sciences, Princess Sumaya University for Technology, Amman, Jordan

    Asma AlNashash

  5. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India

    D. Jude Hemanth

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  1. Sanaa Kaddoura
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Correspondence to Sanaa Kaddoura.

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Kaddoura, S., Alex, S.A., Itani, M. et al. Arabic spam tweets classification using deep learning. Neural Comput & Applic 35, 17233–17246 (2023). https://doi.org/10.1007/s00521-023-08614-w

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