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This paper presents a labeled corpus of tweets related to obstetric violence in Mexico, annotated by narrative type and type of violence. We address the challenges of multi-class and multi-label classification under severe data imbalance using synthetic tweet generation via large language models and specialized loss functions. Our results show that RoBERTuito achieves the best performance, and that both data augmentation and loss adaptation improve classification metrics. This work contributes resources and methods for the automatic detection of obstetric violence in social media discourse.

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Automatic Synthetic Data Selection for Highly Imbalanced Multi-class and Multi-label Obstetric Violence Classification

  • 会议论文

  • 发表日期：

    2026年

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  • Natalia Lerín-Hernández,
  • Jorge Téllez-Torres,
  • Helena Gomez-Adorno,
  • Mónica Vázquez-Hernández

摘要

This paper presents a labeled corpus of tweets related to obstetric violence in Mexico, annotated by narrative type and type of violence. We address the challenges of multi-class and multi-label classification under severe data imbalance using synthetic tweet generation via large language models and specialized loss functions. Our results show that RoBERTuito achieves the best performance, and that both data augmentation and loss adaptation improve classification metrics. This work contributes resources and methods for the automatic detection of obstetric violence in social media discourse.