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Title: Anàlisi de les dades del Dia Mundial de les Malalties Minoritàries a Twitter
Author: Dalmases Juanet, Joaquim Maria de
Tutor: Subirats, Laia  
Bonet-Carne, Elisenda  
Others: Prados Carrasco, Ferran  
Abstract: Minority diseases are a social problem that affects basic humanitarian rights such as social equality. Most patients suffering from them are helpless to cope with them. To fight against them, it is necessary to define support for research, drug development, networking among patient groups, to intensify the fight, awareness raising and many other organized social actions. With the aim of reducing the impact of rare diseases on the lives of patients and families, this work characterizes the content of Twitter data captured around the World Rare Disease Day 2020 to act in this direction. This characterization of social data will be performed from 2 points of view, structural and content analysis. Unsupervised machine learning techniques will be applied to find existing emerging user communities on this topic. The availability of user communities will allow us to give voice and enhance all the aspects that unite them and to have a criterion for decision making towards their current level of attention and the necessary in the future. Another challenge is the structuring of practical measures and recommendations of actions towards the conclusions of the analysis oriented towards the support of patients and the fight against minority diseases.
Keywords: Twitter
rare diseases
unsupervised learning
Document type: info:eu-repo/semantics/masterThesis
Issue Date: Jun-2020
Appears in Collections:Bachelor thesis, research projects, etc.

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quimdalmasesTFM0620presentació.pdfPresentació del TFM5,12 MBAdobe PDFThumbnail
quimdalmasesTFM0620memoria.pdfMemoria del TFM11,42 MBAdobe PDFThumbnail