Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach.
A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD dataset that reflects both the clinical insights and the distribution of depression symptoms from the samples of self-di...
| Publicado en: | Language Resources & Evaluation Vol. 58; no. 3; pp. 1013 - 1042 |
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| Autores principales: | , , , |
| Formato: | Artículo |
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Springer Nature
Sep2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=179813864&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 179813864 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Sep2024 vid: 58 iid: 3 pid: 237 pub: Springer Nature artinfo: ui: 179813864 10.1007/s10579-024-09720-4 ppf: 1013 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.3MB tig: atl: Depression symptoms modelling from social media text: an LLM driven semi-supervised learning approach. aug: au: Farruque, Nawshad Goebel, Randy Sivapalan, Sudhakar Zaïane, Osmar R. affil: https://ror.org/0160cpw27 Department of Computing Science, Faculty of Science, Alberta Machine Intelligence Institute (AMII), University of Alberta, T6G 2E8, Edmonton, AB, Canada https://ror.org/0160cpw27 Department of Psychiatry, Faculty of Medicine and Dentistry, University of Alberta, T6G 2H5, Edmonton, AB, Canada su: Language models Supervised learning Mental depression Social media sug: subj: Language models Supervised learning Mental depression Social media keyword: Bidirectional Encoder Representations from Transformers (BERT) Depression detection Depression symptoms detection Mental-BERT Semi-supervised learning Zero-shot learning ab: A fundamental component of user-level social media language based clinical depression modelling is depression symptoms detection (DSD). Unfortunately, there does not exist any DSD dataset that reflects both the clinical insights and the distribution of depression symptoms from the samples of self-disclosed depressed population. In our work, we describe a semi-supervised learning (SSL) framework which uses an initial supervised learning model that leverages (1) a state-of-the-art large mental health forum text pre-trained language model further fine-tuned on a clinician annotated DSD dataset, (2) a Zero-Shot learning model for DSD, and couples them together to harvest depression symptoms related samples from our large self-curated depressive tweets repository (DTR). Our clinician annotated dataset is the largest of its kind. Furthermore, DTR is created from the samples of tweets in self-disclosed depressed users Twitter timeline from two datasets, including one of the largest benchmark datasets for user-level depression detection from Twitter. This further helps preserve the depression symptoms distribution of self-disclosed tweets. Subsequently, we iteratively retrain our initial DSD model with the harvested data. We discuss the stopping criteria and limitations of this SSL process, and elaborate the underlying constructs which play a vital role in the overall SSL process. We show that we can produce a final dataset which is the largest of its kind. Furthermore, a DSD and a Depression Post Detection model trained on it achieves significantly better accuracy than their initial version. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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