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Comparación de comunidades de redes sociales mediante análisis de datos funcionales

Xiaoxia Champon1, Jasser Jasser2, Chathura Jayalath3

  • 1North Carolina State University.

Proceedings of the ... Annual Hawaii International Conference on System Sciences. Annual Hawaii International Conference on System Sciences
|December 22, 2025
PubMed
Resumen

Las comunidades de redes sociales divergen, dando forma al flujo de información. El análisis de datos temporales de publicaciones y retuits revela estrategias de desinformación y disparidades en las comunidades, crucial para comprender la difusión de información.

Palabras clave:
Redes Socialesanálisis de datos funcionalesdiferencias de grupo

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Área de la Ciencia:

  • Ciencias Sociales
  • Ciencias Sociales Computacionales
  • Ciencia de la Información

Sus antecedentes:

  • Las plataformas de redes sociales albergan diversas comunidades con trayectorias conversacionales divergentes.
  • La desinformación y los mensajes manipulados pueden influir significativamente en la opinión pública.
  • Comprender las disparidades intergrupales es clave para analizar el flujo de información.

Objetivo del estudio:

  • Cuantificar las disparidades entre comunidades opuestas de redes sociales.
  • Descubrir estrategias distintas utilizadas por estas comunidades para la promoción de campañas.
  • Analizar la dinámica temporal de la difusión de información en grupos en línea.

Principales métodos:

  • Se utilizó el análisis de datos funcionales para examinar la dinámica temporal de los grupos de redes sociales.
  • Se evaluó el comportamiento grupal utilizando métricas dependientes del tiempo como publicaciones y retuits.
  • Se investigaron datos de Twitter relacionados con incidentes de alto perfil (Skripal/Novichok, Crímenes de Bucha).

Principales resultados:

  • Los hallazgos preliminares resaltan diferencias cuantificables en las estrategias de difusión de información entre comunidades.
  • Identificadas patrones temporales en la actividad de publicaciones y retuits que se correlacionan con campañas específicas.
  • Reveladas distintas mecánicas de comportamiento empleadas por grupos en línea opuestos.

Conclusiones:

  • El estudio ofrece nuevas perspectivas sobre la mecánica de la difusión de información en las redes sociales.
  • La cuantificación de las disparidades comunitarias ayuda a comprender la propagación de información y desinformación.
  • Los hallazgos pueden informar estrategias para tiempos de respuesta óptimos a las campañas en línea.