On the incidence of depression symptoms on social media

Esteban A. Ríssola (), Mario Ezra Aragón (), David E. Losada () and Fabio Crestani ()
Additional contact information
Esteban A. Ríssola: Universidad Nacional de Luján (UNLu)
Mario Ezra Aragón: Universidade de Santiago de Compostela (USC)
David E. Losada: Universidade de Santiago de Compostela (USC)
Fabio Crestani: Università della Svizzera italiana (USI)

Journal of Computational Social Science, 2025, vol. 8, issue 2, No 22, 30 pages

Abstract: Abstract Due to their increasing popularity, researchers and health professionals are actively utilizing social media networks as valuable tools to recognize linguistic patterns associated with mental health. In this research, our aim was to better understand to what extent the Beck Depression Inventory (BDI) could undergo automated screening based on users’ social media feeds. To this end, we conducted different experiments to analyze the prevalence of BDI items on social media. We present an approach to categorizing and ranking BDI items considering the quantity of information that can be obtained from social media posts. Given publications written by people who have personally reported being diagnosed with depression, we run different search methods and, based on the number of elements retrieved, we study the prevalence of BDI symptoms at two levels of coverage. Finally, we investigate the impact of prevalence and various characteristics on the efficacy of automated assessment tools. Our analysis indicates that specific elements occur consistently across various search methods and social media platforms, implying a higher prevalence of related symptoms in the data sets analyzed. Interestingly, some items with low incidence in the data sets are those of the BDI questionnaire, whose responses are more accurately estimated using automated methods.

Keywords: Health informatics; Information retrieval; Social media mining; Depression; Beck Depression Inventory (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42001-025-00377-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jcsosc:v:8:y:2025:i:2:d:10.1007_s42001-025-00377-9

Ordering information: This journal article can be ordered from
http://www.springer. ... iences/journal/42001

DOI: 10.1007/s42001-025-00377-9

Access Statistics for this article

Journal of Computational Social Science is currently edited by Takashi Kamihigashi

More articles in Journal of Computational Social Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().