A Hybrid AI System for Evaluating Media Representation of Violence and Inequality
| Type of publication: | Inproceedings |
| Citation: | |
| Booktitle: | 26th International Conference on Web Information Systems Engineering |
| Volume: | 16368 |
| Year: | 2025 |
| Month: | December |
| Pages: | 194-209 |
| Publisher: | Springer Nature |
| Location: | Marrakech, Morocco |
| ISBN: | 978-981-95-7251-9 |
| URL: | https://link.springer.com/chap... |
| 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. |
| Keywords: | gender-based violence, Hybrid AI, Large Language Models, Natural Language Processing, Social bias in media representation, Web text mining |
| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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