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Detección de ciberacoso en contenido de video de formato corto impulsada por IA: Un marco híbrido de aprendizaje

Ahmad A Mazhar¹, Islam Zada², Manal Aldhayan³

¹College of Communication, University of Sharjah, Sharjah, United Arab Emirates.

|February 11, 2026

Resumen

Este estudio introduce un marco híbrido de aprendizaje profundo para detectar el acoso cibernético en videos cortos mediante el análisis de señales visuales, de audio y de texto. El modelo logra una alta precisión, ofreciendo potencial para la moderación automatizada de contenido.

Área de la Ciencia:

La inteligencia artificial es inteligencia artificial.
Ciberseguridad Ciberseguridad.
Análisis Multimedia Análisis Multimedia.

Sus antecedentes:

Las plataformas de video de formato corto (por ejemplo, TikTok, Instagram Reels) han aumentado en popularidad.
El acoso cibernético es un problema creciente en estas plataformas, a menudo utilizando señales multimodales.
Los métodos de detección existentes luchan con la complejidad del acoso basado en video.

Objetivo del estudio:

Desarrollar un marco sólido para la detección del acoso cibernético en vídeos de formato corto multimodal.
Para abordar el desafío de analizar datos de video, audio y texto sincronizados para la detección de acoso.
Mejorar la precisión y confiabilidad de los sistemas automatizados de moderación de contenido.

Principales métodos:

Un enfoque híbrido de aprendizaje profundo que integra redes neuronales convolucionales (CNN), redes bidireccionales de memoria de largo corto plazo (BiLSTM) y codificadores de transformador.
Análisis de las características espaciales (CNNs), patrones acústicos temporales (BiLSTM) y contenido textual (Transformer).
Implementación de una capa de validación de consistencia semántica con restricciones basadas en la atención para la alineación intermodal.

Principales resultados:

Rendimiento de última generación en conjuntos de datos de referencia (CAVD, SocialVidMix) con una alta precisión (91,6%), precisión (89,7%), recuerdo (93,0%) y puntaje F1 (91,3%).
Resultados consistentes en diferentes plataformas, incluidos Instagram, TikTok y YouTube Shorts.
Interpretabilidad demostrada, robustez y escalabilidad del marco propuesto.

Conclusiones:

El marco híbrido de aprendizaje profundo detecta efectivamente el acoso cibernético en vídeos de formato corto multimodal.
La capa de consistencia semántica mejora la detección al garantizar la alineación intermodal.
El marco muestra un potencial significativo para la implementación en tiempo real en sistemas automatizados de moderación de contenido.

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