Please use this identifier to cite or link to this item:
Title: Clasificador de información en Salud de YouTube
Author: Belenguer Querol, Laura
Tutor: Sanchez-Bocanegra, Carlos Luis  
Others: Fernández Sierra, Alberto
Keywords: COVID-19
social networks
Issue Date: 1-Jun-2022
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: Nowadays, both patients and healthcare professionals use social networks to find out about diseases and treatments. These channels are not unidirectional, as they allow users to exchange experiences and opinions. They offer immediacy and access to a large community with the same interests in certain health topics. The potential of social networks for the dissemination of health information is evident. However, the information published lacks the rigor of scientific publications and it is difficult to determine the veracity of the information disseminated in videos and comments. In this paper, we present a methodology based on natural language processing (NLP) to analyze video texts and named entity recognition (NER) to identify relevant terms in coronaviruses.
Language: Spanish
Appears in Collections:Bachelor thesis, research projects, etc.

Files in This Item:
File Description SizeFormat 
lbelenguerTFM0622memoria.pdfMemoria del TFM4,04 MBAdobe PDFThumbnail