Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter

Detalhes bibliográficos
Autor(a) principal: Gael Pérez-Rodríguez
Data de Publicação: 2020
Outros Autores: Martín Pérez-Pérez, Florentino Fdez-Riverola, Lourenço, Anália
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/1822/65086
Resumo: Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm.
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spelling Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in TwitterSociomeCommunity detectionTopic modellingKnowledge graphsDiabetesTwitterScience & TechnologySupplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm.In recent years, the number of active users in social media has grown exponentially. Despite the thematic diversity of the messages, social media have become an important vehicle to disseminate health information as well as to gather insights about patients experiences and emotional intelligence. Therefore, the present work proposes a new methodology of analysis to identify and interpret the behaviour, perceptions and appreciations of patients and close relatives towards a health condition through their social interactions. At the core of this methodology are techniques of natural language processing and machine learning as well as the reconstruction of knowledge graphs, and further graph mining. The case study is the diabetes community, and more specifically, the patients communicating about type 1 diabetes (T1D) and type 2 diabetes (T2D). The results produced in this study show the effectiveness of the proposed method to discover useful and non-trivial knowledge about patient perceptions of disease. Such knowledge may be used in the context of Health Informatics to promote healthy lifestyles in more efficient ways as well as to improve communication with the patients.This work was partially supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, COMPETE 2020 (POCI-01-0145-FEDER-006684), the Xunta de Galicia (Centro singular de investigación de Galicia accreditation 2019–2022) and the European Union (European Regional Development Fund - ERDF)- Ref. ED431G2019/06, and Consellería de Educación, Universidades e Formación Profesional (Xunta de Galicia) under the scope of the strategic funding of ED431C2018/55-GRC Competitive Reference Group. The authors also acknowledge the Postdoc contract of Martín Pérez-Pérez, funded by the Xunta de Galicia.info:eu-repo/semantics/publishedVersionElsevierUniversidade do MinhoGael Pérez-RodríguezMartín Pérez-PérezFlorentino Fdez-RiverolaLourenço, Anália2020-092020-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/65086engGael Pérez-Rodríguez; Martín Pérez-Pérez; Florentino Fdez-Riverola; Lourenço, Anália, Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter. Future Generation Computer Systems, 110, 214-232, 20200167-739X10.1016/j.future.2020.04.025https://www.journals.elsevier.com/future-generation-computer-systemsinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-21T12:01:11Zoai:repositorium.sdum.uminho.pt:1822/65086Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:51:06.201499Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
title Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
spellingShingle Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
Gael Pérez-Rodríguez
Sociome
Community detection
Topic modelling
Knowledge graphs
Diabetes
Twitter
Science & Technology
title_short Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
title_full Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
title_fullStr Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
title_full_unstemmed Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
title_sort Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter
author Gael Pérez-Rodríguez
author_facet Gael Pérez-Rodríguez
Martín Pérez-Pérez
Florentino Fdez-Riverola
Lourenço, Anália
author_role author
author2 Martín Pérez-Pérez
Florentino Fdez-Riverola
Lourenço, Anália
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Gael Pérez-Rodríguez
Martín Pérez-Pérez
Florentino Fdez-Riverola
Lourenço, Anália
dc.subject.por.fl_str_mv Sociome
Community detection
Topic modelling
Knowledge graphs
Diabetes
Twitter
Science & Technology
topic Sociome
Community detection
Topic modelling
Knowledge graphs
Diabetes
Twitter
Science & Technology
description Supplementary material related to this article can be found online at https://doi.org/10.1016/j.future.2020.04.025.Supplementary material 1: this file contains the 23 user communities detected using the GLay algorithm.
publishDate 2020
dc.date.none.fl_str_mv 2020-09
2020-09-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/65086
url http://hdl.handle.net/1822/65086
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Gael Pérez-Rodríguez; Martín Pérez-Pérez; Florentino Fdez-Riverola; Lourenço, Anália, Mining the sociome for Health Informatics: Analysis of therapeutic lifestyle adherence of diabetic patients in Twitter. Future Generation Computer Systems, 110, 214-232, 2020
0167-739X
10.1016/j.future.2020.04.025
https://www.journals.elsevier.com/future-generation-computer-systems
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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