Year: 2023
Type: article
Study design: Observational Study
Abstract: Loneliness is an increasingly prevalent condition with many adverse effects on health and quality of life. Accordingly, there is a growing interest in developing automated or low-cost methods for triaging and supporting individuals encountering psychosocial distress. This study marks an early attempt at building predictive models to detect loneliness automatically using the digital traces of individuals’ online behavior (Google search and YouTube consumption). Based on a longitudinal study with 92 adult participants for eight weeks in 2021, we find that users’ online behavior can help create automated classification tools for loneliness with high accuracy. Furthermore, we observed behavioral differences in digital traces across platforms. The “not lonely” participants had higher aggregated YouTube activity and lower aggregated Google search activity than “lonely” participants. Our results indicate the need for a further platform-aware exploration of technology use for studies interested in developing automated assessment tools for psychological well-being.
Source: Electronics
Authors: Eiman Ahmed, Liyang Xue, Aniket Sankalp, Haein Kong, Arcadio Matos, Vincent Silenzio, Vivek K. Singh
Language: English
Topic: Mental Health via Writing
Subfield: Social Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,224
OpenAlex ID: W4389143744