A Hybrid AI System for Evaluating Media Representation of Violence and Inequality
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Buchtitel: | 26th International Conference on Web Information Systems Engineering |
| Band: | 16368 |
| Jahr: | 2025 |
| Monat: | Dezember |
| Seiten: | 194-209 |
| Verlag: | Springer Nature |
| Ort: | 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 |
| Abriss: | 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. |
| Schlagworte: | gender-based violence, Hybrid AI, Large Language Models, Natural Language Processing, Social bias in media representation, Web text mining |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
|
Anhänge
|
|
|
|
|
Notizen
|
|
|
|
|
|
Themen
|
|
|
|
