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@INPROCEEDINGS{,
     author = {Liu, Zhan and Glassey Balet, Nicole},
   keywords = {gender-based violence, Hybrid AI, Large Language Models, Natural Language Processing, Social bias in media representation, Web text mining},
      month = dec,
      title = {A Hybrid AI System for Evaluating Media Representation of Violence and Inequality},
  booktitle = {26th International Conference on Web Information Systems Engineering},
     volume = {16368},
       year = {2025},
      pages = {194-209},
  publisher = {Springer Nature},
   location = {Marrakech, Morocco},
       isbn = {978-981-95-7251-9},
        url = {https://link.springer.com/chapter/10.1007/978-981-95-7251-9_14},
        doi = {https://doi.org/10.1007/978-981-95-7251-9_14},
   abstract = {Media coverage of gender-based violence plays a critical role in shaping public understanding and policy, yet often perpetuates stereotypes and biases. We present a hybrid AI approach to analyze how French-language media represent gender-based violence. Combining rule-based Natural Language Processing (NLP) with Large Language Models (LLMs), the system applies expert-defined criteria across analytical categories, with each criterion assigned to the most effective method based on empirical performance. This strategy achieves 87.1\% overall accuracy, surpassing previous models. GPT-4 led general performance (77.9\%), while NLP delivered exceptional results in structural and language-sensitive categories. Our findings demonstrate that combining complementary AI techniques enables near-human accuracy in evaluating media narratives and contributes to advancing web-based text mining for socially relevant media analysis.}
}