Social media language analysis for early detection of mental health disorders

• Grant number: 21/08213-0
• Support Opportunities: Regular Research Grants
• Start date: May 01, 2022
• End date: July 31, 2024
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Ivandre Paraboni
• Grantee: Ivandre Paraboni
• Host Institution: Escola de Artes, Ciências e Humanidades (EACH). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• City of the host institution: São Paulo
• Associated researchers: Deusivania Vieira da Silva Falcão ; Thiago Alexandre Salgueiro Pardo
• Associated research grant(s): 25/10334-0 - 28th International Conference Text Speech and Dialogue (TSD-2025), AR.EXT

Abstract

The observation that individuals with mental health disorders such as depression and anxiety are often regular users of social media has led to the development of a wide range of studies in the Natural Language Processing (NLP) field for risk assessment based on the kind of language employed by these individuals. Existing work in the field is however largely dedicated to the English language, and tends to consider publications (e.g., tweets) produced at any time, including even those produced after the individual is already clinically diagnosed. Thus, models of this kind tend to focus more on the issue of distinguishing individuals with and without a certain disorder, but are less able to anticipate these as a means to prevent their possible aggravation. Based on these observations, this project proposes to explore the temporal information provided by the Twitter platform for the study and development of computational models for early recognition of depression and anxiety disorder in Portuguese using a database - called the SeptemberBR corpus - carefully designed so as to include only texts that are chronologically prior to the date of diagnosis reported by social media users. A study of this kind, in addition to introducing a novel (and possibly more useful) formulation of the present computational problem, opens up the opportunity for a number of scientific contributions in the NLP field, including the modeling of textual and non-textual features and the use of recent neural learning methods, and enables novel solutions for a pressing issue of great social interest. (AU)

Scientific publications

(5)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

DA SILVA, SAMUEL CAETANO; PARABONI, IVANDRE. Politically-oriented information inference from text. JOURNAL OF UNIVERSAL COMPUTER SCIENCE, v. 29, n. 6, p. 26-pg., 2023-01-01. (21/08213-0)

DE OLIVEIRA, RAFAEL LAGE; MARTINS, JOAO TREVISAN; PARABONI, IVANDRE. Mental health prediction from social media connections. New Review of Hypermedia and Multimedia, v. 29, n. 3-4, p. 20-pg., 2024-04-26. (21/08213-0)

DOS SANTOS, WESLEY RAMOS; PARABONI, IVANDRE; MATSUSHIMA, ELTON HIROSHI; DA SILVA, CAMILA AZEVEDO; MEIRA, EMILY SAMARA DE MOURA; GUIMARAES, JOAO VICTOR RODRIGUES FERREIRA; LINS, JULIA DA SILVA; DE AZEREDO, LAURA ENHAM; NUNES, LUIZ GUILHERME CERQUEIRA; REGO, VITTORIA THIENGO SILVEIRA MOREIRA. Mixture of Experts for Depression and Anxiety Disorder Prediction From Textual and Non-Textual Social Media Data. IEEE ACCESS, v. 13, p. 13-pg., 2025-01-01. (21/08213-0)

DOS SANTOS, WESLEY RAMOS; DE OLIVEIRA, RAFAEL LAGE; PARABONI, IVANDRE. BMI trajectory and inflammatory effects on metabolic syndrome in adolescents. Language Resources and Evaluation, v. N/A, p. 28-pg., 2023-01-11. (19/07665-4, 21/08213-0)

DOS SANTOS, WESLEY RAMOS; PARABONI, IVANDRE. Tracking Mental Health Indicators on Social Media Before and After Diagnosis. TEXT, SPEECH, AND DIALOGUE, TSD 2025, PT II, v. 16030, p. 12-pg., 2026-01-01. (21/08213-0)