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Delving into the Depths: Evaluating Depression Severity through BDI-biased Summaries
dc.contributor.author | Aragón, Mario Ezra | |
dc.contributor.author | Parapar, Javier | |
dc.contributor.author | Losada, David E. | |
dc.date.accessioned | 2024-05-06T14:30:28Z | |
dc.date.available | 2024-05-06T14:30:28Z | |
dc.date.issued | 2024-03 | |
dc.identifier.citation | Mario Aragon, Javier Parapar, and David E Losada. 2024. Delving into the Depths: Evaluating Depression Severity through BDI-biased Summaries. In Proceedings of the 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024), pages 12–22, St. Julians, Malta. Association for Computational Linguistics. | es_ES |
dc.identifier.isbn | 979-8-89176-093-6 | |
dc.identifier.uri | http://hdl.handle.net/2183/36414 | |
dc.description.abstract | [Abstract]: Depression is a global concern suffered by millions of people, significantly impacting their thoughts and behavior. Over the years, heightened awareness, spurred by health campaigns and other initiatives, has driven the study of this disorder using data collected from social media platforms. In our research, we aim to gauge the severity of symptoms related to depression among social media users. The ultimate goal is to estimate the user’s responses to a well-known standardized psychological questionnaire, the Beck Depression Inventory-II (BDI). This is a 21-question multiple-choice self-report inventory that covers multiple topics about how the subject has been feeling. Mining users’ social media interactions and understanding psychological states represents a challenging goal. To that end, we present here an approach based on search and summarization that extracts multiple BDI-biased summaries from the thread of users’ publications. We also leverage a robust large language model to estimate the potential answer for each BDI item. Our method involves several steps. First, we employ a search strategy based on sentence similarity to obtain pertinent extracts related to each topic in the BDI questionnaire. Next, we compile summaries of the content of these groups of extracts. Last, we exploit chatGPT to respond to the 21 BDI questions, using the summaries as contextual information in the prompt. Our model has undergone rigorous evaluation across various depression datasets, yielding encouraging results. The experimental report includes a comparison against an assessment done by expert humans and competes favorably with state-of-the-art methods. | es_ES |
dc.description.sponsorship | The second author thanks the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G/01, ED431B 2022/33) and the European Regional Development Fund, which acknowledges the CITIC Research Center in ICT of the University of A Coruña as a Research Center of the Galician University System and the project PID2022-137061OB-C21 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The first and third authors thank: i) the financial support supplied by the Consellería de Cultura, Educación, Formación Profesional e Universidades (accreditation 2019-2022 ED431G-2019/04, ED431C 2022/19) and the European Regional Development Fund, which acknowledges the CiTIUS- Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University System, and ii) the financial support supplied by project PID2022-137061OB-C22 (Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Proyectos de Generación de Conocimiento; supported by the European Regional Development Fund). The third author thanks the financial support obtained from project SUBV23/00002 (Ministerio de Consumo, Subdirección General de Regulación del Juego). The authors also thank the funding of project PLEC2021-007662 (MCIN/AEI/10.13039/501100011033, Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación, Plan de Recuperación, Transformación y Resiliencia, Unión Europea-Next Generation EU). | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431B 2022/33 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G-2019/04 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2022/19 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Association for Computational Linguistics | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2022-137061OB-C21/ES/BUSQUEDA, SELECCION Y ORGANIZACION DE CONTENIDOS PARA NECESIDADES DE INFORMACION RELACIONADAS CON LA SALUD - CONSTRUCCION DE RECURSOS Y PERSONALIZACION | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2022-137061OB-C22/ES/BUSQUEDA, SELECCION Y ORGANIZACION DE CONTENIDOS PARA NECESIDADES DE INFORMACION RELACIONADAS CON LA SALUD: BUSQUEDA Y DETECCION DE DESINFORMACION | es_ES |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2024/PLEC2021-007662/ES/BIG-eRISK: PREDICCIÓN TEMPRANA DE RIESGOS PERSONALES EN CONJUNTOS DE DATOS MASIVOS | es_ES |
dc.relation | info:eu-repo/grantAgreement/MIC/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/SUBV23%2F00002/ES/ | es_ES |
dc.relation.uri | https://aclanthology.org/2024.clpsych-1.2/ | es_ES |
dc.rights | © 2024 ACL | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | Social media | es_ES |
dc.subject | Computational Intelligence | es_ES |
dc.subject | eRisk | es_ES |
dc.subject | Mental Health | es_ES |
dc.subject | Social Media Monitoring | es_ES |
dc.subject | Language Models | es_ES |
dc.title | Delving into the Depths: Evaluating Depression Severity through BDI-biased Summaries | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.volume | Proceedings | es_ES |
UDC.startPage | 12 | es_ES |
UDC.endPage | 22 | es_ES |
UDC.conferenceTitle | 9th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2024) | es_ES |