Detecting Chinese Sexism Text in Social Media Using Hybrid Deep Learning Model with Sarcasm Masking

Sexist content is prevalent in social media, which seriously affects the online environment and occasionally leads to offline disputes. For this reason, many scholars have researched how to automatically detect sexist content in social media. However, the presence of sarcasm complicates this task. Thus, recognizing sarcasm to improve the accuracy of sexism detection has become a crucial research focus. In this study, we adopt a deep learning approach by combining a sexism lexicon and a sarcasm lexicon to work on the detection of Chinese sexist content in social media. We innovatively propose a sarcasm-based masking mechanism, which achieves an accuracy of 82.65% and a macro F1 score of 80.49% on the Sina Weibo Sexism Review (SWSR) dataset, significantly outperforming the baseline model by 2.05% and 2.89%, respectively. This study combines the irony masking mechanism with sexism detection, and the experimental results demonstrate the effectiveness of the deep learning method based on the irony masking mechanism in Chinese sexism detection.

@article{Wang2025,
  title     = {Detecting Chinese Sexism Text in Social Media Using Hybrid Deep Learning Model with Sarcasm Masking},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Lei Wang and Nur Atiqah Sia Abdullah and Syaripah Ruzaini Syed Aris},
  doi       = {10.14569/IJACSA.2025.01602107},
  url       = {https://doi.org/10.14569/IJACSA.2025.01602107}
}