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Predictive analysis of COVID-19 symptoms in social networks through machine learning

2022Journal article Open access
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dc.contributor.authorSilva, Clístenes Fernandes da
dc.contributor.authorJunior, Arnaldo Candido
dc.contributor.authorLopes, Rui Pedro
dc.date.accessioned2022-04-04T10:28:25Z
dc.date.available2022-04-04T10:28:25Z
dc.date.issued2022
dc.description.abstractSocial media is a great source of data for analyses, since they provide ways for people to share emotions, feelings, ideas, and even symptoms of diseases. By the end of 2019, a global pandemic alert was raised, relative to a virus that had a high contamination rate and could cause respiratory complications. To help identify those who may have the symptoms of this disease or to detect who is already infected, this paper analyzed the performance of eight machine learning algorithms (KNN, Naive Bayes, Decision Tree, Random Forest, SVM, simple Multilayer Perceptron, Convolutional Neural Networks and BERT) in the search and classification of tweets that mention self-report of COVID-19 symptoms. The dataset was labeled using a set of disease symptom keywords provided by the World Health Organization. The tests showed that Random Forest algorithm had the best results, closely followed by BERT and Convolution Neural Network, although traditional machine learning algorithms also have can also provide good results. This work could also aid in the selection of algorithms in the identification of diseases symptoms in social media content.pt_PT
dc.description.sponsorshipThis work has been supported by FCT—Fundação para a Ciência e Tecnologia within the Project Scope: DSAIPA/AI/0088/2020pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationSilva, Clístenes Fernandes da; Junior, Arnaldo Candido; Lopes, Rui Pedro (2022). Predictive analysis of COVID-19 symptoms in social networks through machine learning. Electronics. ISSN 2079-9292. 11:4, p. 1-14pt_PT
dc.identifier.doi10.3390/electronics11040580pt_PT
dc.identifier.eissn2079-9292
dc.identifier.urihttp://hdl.handle.net/10198/25323
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.relationPandIA - Management of Pandemic Social Isolation Based on City and Social Intelligence
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectNatural language processingpt_PT
dc.subjectMachine learningpt_PT
dc.subjectText classificationpt_PT
dc.subjectCOVID-19pt_PT
dc.subjectTweet analysispt_PT
dc.titlePredictive analysis of COVID-19 symptoms in social networks through machine learningpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitlePandIA - Management of Pandemic Social Isolation Based on City and Social Intelligence
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FAI%2F0088%2F2020/PT
oaire.citation.issue4pt_PT
oaire.citation.startPage580pt_PT
oaire.citation.titleElectronicspt_PT
oaire.citation.volume11pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameLopes
person.givenNameRui Pedro
person.identifier.ciencia-id8E14-54E4-4DB5
person.identifier.orcid0000-0002-9170-5078
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
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relation.isAuthorOfPublication.latestForDiscoverye1e64423-0ec8-46ee-be96-33205c7c98a9
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relation.isProjectOfPublication.latestForDiscovery6f2b3abd-6e7e-4629-a3a0-469f3ae3a919

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