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Internet-based identification of anxiety in university students using text and facial emotion analysis

2023-12Artigo científico Acesso aberto
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datacite.subject.fosCiências Médicas::Outras Ciências Médicas
datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorGuerrero, Graciela
dc.contributor.authorAvila, Daniel
dc.contributor.authorSilva, Fernando José Mateus da
dc.contributor.authorPereira, António
dc.contributor.authorFernández-Caballero, Antonio
dc.date.accessioned2026-10-07T16:08:26Z
dc.date.available2026-10-07T16:08:26Z
dc.date.issued2023-12
dc.description.abstractBackground: Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students. Objective: The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data. Methods: The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety. Results: Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %). Conclusions: The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.eng
dc.description.sponsorshipGrants PID2020-115220RB-C21 and EQC2019-006063-P funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way to make Europe”. Grant 2022-GRIN-34436 funded by Universidad de Castilla-La Mancha and by “ERDF A way of making Europe”. This research was also supported by CIBERSAM, Instituto de Salud Carlos III, and Ministerio de Ciencia e Innovación. This work has been partially supported by Portuguese Fundação para a Ciência e a Tecnologia – FCT, I.P. under the project UIDB/04524/2020 and by Portuguese National funds through FITEC - Programa Interface, with reference CIT “INOV - INESC Inovação - Financiamento Base”.
dc.identifier.citationGraciela Guerrero, Daniel Avila, Fernando José Mateus da Silva, António Pereira, Antonio Fernández-Caballero, Internet-based identification of anxiety in university students using text and facial emotion analysis, Internet Interventions, Volume 34, 2023, 100679, ISSN 2214-7829, https://doi.org/10.1016/j.invent.2023.100679.
dc.identifier.doi10.1016/j.invent.2023.100679
dc.identifier.eissn2214-7829
dc.identifier.urihttp://hdl.handle.net/10400.8/16989
dc.language.isoeng
dc.peerreviewedyes
dc.publisherElsevier
dc.relationResearch Center in Informatics and Communications
dc.relation.hasversionhttps://www.sciencedirect.com/science/article/pii/S2214782923000799?via%3Dihub
dc.relation.ispartofInternet Interventions
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAnxiety
dc.subjectUniversity students
dc.subjectAdult manifest anxiety scale–college version (AMAS-C)
dc.subjectFacial expression analysis
dc.subjectText sentiment analysis
dc.titleInternet-based identification of anxiety in university students using text and facial emotion analysiseng
dc.typejournal article
dspace.entity.typePublication
oaire.awardNumberUIDB/04524/2020
oaire.awardTitleResearch Center in Informatics and Communications
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04524%2F2020/PT
oaire.citation.endPage12
oaire.citation.startPage1
oaire.citation.titleInternet Interventions
oaire.citation.volume34
oaire.fundingStream6817 - DCRRNI ID
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.affiliation.nameEng. Informática
person.familyNameSilva
person.familyNamePereira
person.givenNameFernando
person.givenNameAntónio
person.identifier.ciencia-id9D19-84F9-F1CA
person.identifier.ciencia-idE215-4F0F-33EC
person.identifier.orcid0000-0001-9335-1851
person.identifier.orcid0000-0001-5062-1241
person.identifier.ridM-6163-2013
person.identifier.scopus-author-id24402946400
person.identifier.scopus-author-id7402230199
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
relation.isAuthorOfPublication2db213d9-a071-4f43-9544-1295ebb6ffde
relation.isAuthorOfPublication6320b167-2323-4699-bf04-9288d3f603c0
relation.isAuthorOfPublication.latestForDiscovery2db213d9-a071-4f43-9544-1295ebb6ffde
relation.isProjectOfPublication67435020-fe0d-4b46-be85-59ee3c6138c7
relation.isProjectOfPublication.latestForDiscovery67435020-fe0d-4b46-be85-59ee3c6138c7

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Background: Anxiety in university students can lead to poor academic performance and even dropout. The Adult Manifest Anxiety Scale (AMAS-C) is a validated measure designed to assess the level and nature of anxiety in college students. Objective: The aim of this study is to provide internet-based alternatives to the AMAS-C in the automated identification and prediction of anxiety in young university students. Two anxiety prediction methods, one based on facial emotion recognition and the other on text emotion recognition, are described and validated using the AMAS-C Test Anxiety, Lie and Total Anxiety scales as ground truth data. Methods: The first method analyses facial expressions, identifying the six basic emotions (anger, disgust, fear, happiness, sadness, surprise) and the neutral expression, while the students complete a technical skills test. The second method examines emotions in posts classified as positive, negative and neutral in the students' profile on the social network Facebook. Both approaches aim to predict the presence of anxiety. Results: Both methods achieved a high level of precision in predicting anxiety and proved to be effective in identifying anxiety disorders in relation to the AMAS-C validation tool. Text analysis-based prediction showed a slight advantage in terms of precision (86.84 %) in predicting anxiety compared to face analysis-based prediction (84.21 %). Conclusions: The applications developed can help educators, psychologists or relevant institutions to identify at an early stage those students who are likely to fail academically at university due to an anxiety disorder.
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