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The CLEAR.TEXT project (TED2021-130707B-I00) researches how natural language processing technology can support the authoring of accessible content in Spanish for people with cognitive disabilities. Our main objective is to research, implement, deploy, evaluate, and ultimately provide robust technologies for natural language processing to support the authoring of accessible Spanish content for public sector organisations (at local, regional and national level) that is intelligible to people with cognitive disability, thereby widening their inclusion and empowerment in Europe. It is expected to impact positively the quality of life of people with cognitive disabilities, facilitating their access to educational, vocational, cultural, and social opportunities in public sector organisations.

La sociedad avanza cargada de conocimientos nuevos y muy accesibles, que se publican en el mundo virtual. Es una realidad que las Tecnologías de la Información y la Comunicación (TIC) han traído muchos beneficios a nuestras vidas pero también vemos como año tras año aumenta el uso de violencia en plataformas digitales. Nuestro trabajo se enfoca en la creación de recursos que permitan la detección de mensajes violentos en la red social Twitter. Se parte de la creación de una guía de anotación de grano fino para anotar un corpus de mensajes violentos (VIL) con el fin de utilizar herramientas de aprendizaje automático que nos ayuden a detectar automáticamente el problema. Con este corpus se entrenan dos modelos de lenguaje (BETO y RoBERTa base) con los que se alcanza un valor en la métrica F1m de 97.03% y 96.51% clasificando si un tuit es o no violento., Society is moving forward full of new and very accessible knowledge, which is published in the virtual world. It is a reality that ICTs have brought many benefits to our lives but we also see how year after year the use of violence on digital platforms increases. Our work focuses on the detection of violent messages in the social network Twitter. Starting from the creation of a fine-grained annotation guide to obtain a corpus of violent messages (VIL) in order to use Machine Learning tools that help us to automatically detect the problem Two language models are trained with this corpus (BETO and RoBERTa base) with which a value of 97.03% and 96.51% is reached in the F1m metric, classifying whether or not a tweet is violent.