AraSpam: A Multitask Deep Neural Network for Spam Detection in Arabic Twitter

Twitter has become widely used for disseminating information across the Arab world. It provides diverse communicative and informational needs while serving as a rich data source for a wide range of research. However, the integrity of such data is frequently undermined by the pervasive issue of spam. Existing research proposed the use of spam detection models at multiple levels—the account, tweet, and campaign levels. Many of these models target Uniform Resource Locator (URL)-based spam messages, whereas a significant portion of spam content operates without embedded URLs. Furthermore, spam detection methodologies tailored to the account level often lack the precision required for tweet-level analysis or, conversely, fail to capture broader account-level behavioral patterns. Moreover, studies focusing on Arabic spam have largely been restricted to specific geographical regions or linguistic varieties, such as Arabic dialect (AD) or Modern Standard Arabic (MSA), thereby neglecting the full spectrum of Arabic’s linguistic diversity in spam messages. This study aims to address these limitations by proposing AraSpam, a multitask deep neural network that detects both spam messages and profiles using a single model. It was trained using a dataset of tweets written in AD and MSA covering different spamming targets. The text features were extracted using transformer-based models: AraBERT for tweet text and mBERT for profile screen name. The experiment demonstrated 96% accuracy in detecting both spam accounts and tweets with seven different spamming targets. Additionally, the experiments revealed that reducing the number of spam classes resulted in an increase in tweet detection performance and a decrease at the account level.

@article{Alhamdan2025,
  title     = {AraSpam: A Multitask Deep Neural Network for Spam Detection in Arabic Twitter},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Lulua Alhamdan and Ahmed Alsanad and Nora Al-Twairesh},
  doi       = {10.14569/IJACSA.2025.0160816},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160816}
}