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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
Year: 2025
Month: December
Publisher: Springer Nature
Location: Marrakech, Morocco
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 Liu, Zhan
Glassey Balet, Nicole
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Total mark: 0
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