Compiling a Corpus of User-Generated Content Units for the Detection of Social Problems

• Autores: Rocío Jiménez Briones
• Localización: RAEL: revista electrónica de lingüística aplicada, ISSN 1885-9089, Vol. 23, Nº. 1, 2024, págs. 1-18
• Idioma: inglés
• Títulos paralelos:
  • Creación de un corpus de contenido generado por los usuarios para la detección de problemas sociales
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Plataformas como Facebook, X e Instagram proporcionan información muy valiosa sobre problemas cotidianos y globales gracias al contenido generado por sus usuarios, ya que estos se convierten en sensores sociales capaces de identificar problemas como la violencia contra las mujeres o los peligros naturales. Dentro del campo del crowdsensing, cuyo objetivo es extraer información útil de estos sensores sociales, presentamos una propuesta de identificación de problemas en ALLEGRO (http://allegro.ucam.edu/). Basándonos en estudios previos sobre este sistema, se explica el marco conceptual para abordar el problema del maltrato institucional a ancianos dentro del módulo de análisis de texto de ALLEGRO o DIAPASON. Se detalla también la metodología y los retos que plantea la compilación de un subcorpus de tuits para dicho problema social, el cual contribuirá al corpus integral de problemas de ALLEGRO que se está construyendo como conjunto de entrenamiento para modelos de aprendizaje profundo en la clasificación de textos

  • English

    Social media platforms like Facebook, X, and Instagram provide valuable information about daily and global social problems through their user-generated content units. Such platforms turn their users into social sensors capable of identifying problems such as violence against women or natural hazards. Within the field of crowdsensing, which aims to extract useful information from these social sensors, we introduce a proposal for identifying problems in ALLEGRO (http://allegro.ucam.edu/). Based on previous studies within this smart multimodal system, our research first explains the conceptual framework for addressing one of these problems, namely, elderly institutional abuse, within the text analysis module of ALLEGRO or DIAPASON. We also detail the methodology and challenges of compiling a subcorpus of tweets related to this problem. Such a specific subcorpus will contribute to ALLEGRO’s comprehensive corpus of social problems, which is being built as a training set for deep learning models in text classification.

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