Detección de Información Falsa sobre Covid-19. Un estudio
Palabras clave:
Detección de Noticias Falsas, Seguridad de la Información, Redes SocialesResumen
El aumento en la difusión de información falsa en plataformas de redes sociales, especialmente en relación con Covid-19, representa una amenaza significativa tanto para el bienestar mental como físico de las personas. Detectar y prevenir la propagación de desinformación es una tarea crucial. Este artículo proporciona una visión general de diversos enfoques empleados para la detección de noticias falsas relacionadas con Covid-19, abarcando modelos de Aprendizaje Automático Clásico, modelos basados en Redes Neuronales y aquellos derivados de metodologías alternativas y pasos de preprocesamiento. El análisis incluye aportes del desafío "Constraint@AAAI2021 - Detección de Noticias Falsas de COVID-19", que tenía como objetivo clasificar binariamente las noticias provenientes de las redes sociales en las categorías de falsas y reales. Examinamos los enfoques más efectivos propuestos por los investigadores durante el desafío. Además, detallamos conjuntos de datos que contienen noticias falsas relacionadas con Covid-19, ofreciendo recursos valiosos para la detección y clasificación de dicha desinformación.
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