dc.creator | Castillo Sánchez, Gema | es |
dc.creator | Marques, Gonçalo | es |
dc.creator | Dorronzoro Zubiete, Enrique | es |
dc.creator | Rivera Romero, Octavio | es |
dc.creator | Franco Martín, Manuel | es |
dc.creator | Torre Díez, Isabel de la | es |
dc.date.accessioned | 2021-03-03T11:19:14Z | |
dc.date.available | 2021-03-03T11:19:14Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Castillo Sánchez, G., Marques, G., Dorronzoro Zubiete, E., Rivera-Romero, O., Franco Martín, M. y Torre Díez, I.d.l. (2020). Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review. Journal of Medical Systems, 44 (art. nº 205), 204-218. | |
dc.identifier.issn | 0148-5598 | es |
dc.identifier.uri | https://hdl.handle.net/11441/105598 | |
dc.description.abstract | According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed
suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper
reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on
social networks. Consequently, the objectives, data collection techniques, development process and the validation
metrics used for suicide detection on social networks are analyzed. The authors conducted a scoping review using
the methodology proposed by Arksey and O’Malley et al. and the PRISMA protocol was adopted to select the
relevant studies. This scoping review aims to identify the machine learning techniques used to predict suicide risk
based on information posted on social networks. The databases used are PubMed, Science Direct, IEEE Xplore and
Web of Science. In total, 50% of the included studies (8/16) report explicitly the use of data mining techniques for
feature extraction, feature detection or entity identification. The most commonly reported method was the Linguistic
Inquiry and Word Count (4/8, 50%), followed by Latent Dirichlet Analysis, Latent Semantic Analysis, and
Word2vec (2/8, 25%). Non-negative Matrix Factorization and Principal Component Analysis were used only in
one of the included studies (12.5%). In total, 3 out of 8 research papers (37.5%) combined more than one of those
techniques. Supported Vector Machine was implemented in 10 out of the 16 included studies (62.5%). Finally, 75%
of the analyzed studies implement machine learning-based models using Python. | es |
dc.description.sponsorship | Ministerio de Industría, Energía y Comercio AAL-20125036 | es |
dc.format | application/pdf | es |
dc.format.extent | 15 | es |
dc.language.iso | eng | es |
dc.publisher | Springer | es |
dc.relation.ispartof | Journal of Medical Systems, 44 (art. nº 205), 204-218. | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Algorithm | es |
dc.subject | Suicide | es |
dc.subject | Social networks | es |
dc.subject | Machine learning | es |
dc.subject | Natural processing language | es |
dc.subject | Data mining | es |
dc.subject | Sentiment analysis | es |
dc.title | Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Tecnología Electrónica | es |
dc.relation.projectID | AAL-20125036 | es |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s10916-020-01669-5 | es |
dc.identifier.doi | 10.1007/s10916-020-01669-5 | es |
dc.journaltitle | Journal of Medical Systems | es |
dc.publication.volumen | 44 | es |
dc.publication.issue | art. nº 205 | es |
dc.publication.initialPage | 204 | es |
dc.publication.endPage | 218 | es |
dc.contributor.funder | Ministerio de Industria, Energía y Comercio. España | es |