RT Journal Article
T1 On the incidence of depression symptoms on social media
A1 Ríssola, Esteban A.
A1 Aragón Saenzpardo, Mario Ezra
A1 Losada Carril, David Enrique
A1 Crestani, Fabio
K1 Health informatics
K1 Information retrieval
K1 Social media mining
K1 Depression
K1 Beck Depression Inventory
AB 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.
PB Springer
YR 2025
FD 2025
LK https://hdl.handle.net/10347/42375
UL https://hdl.handle.net/10347/42375
LA eng
NO Ríssola, E.A., Aragón, M.E., Losada, D.E. et al. On the incidence of depression symptoms on social media. J Comput Soc Sc 8, 48, pp. 1-30 (2025). https://doi.org/10.1007/s42001-025-00377-9
NO Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The Swiss Government Excellence Scholarships and the Hasler Foundation partly supported this work. This research was also funded by Xunta de Galicia and Ministerio de Ciencia e Innovación (Spain).
DS Minerva
RD 30 abr 2026