Measuring Adolescents' Well-being: Correspondence of Naive Digital Traces to Survey Data

TL;DR

This study investigates how digital traces from social media correlate with survey-based measures of adolescents' well-being, revealing weak but meaningful links that suggest digital data can supplement traditional surveys.

Contribution

It provides empirical evidence on the correspondence between digital traces and survey data for adolescent mental health indicators, highlighting their potential as supplementary tools.

Findings

Negative social media sentiment correlates with depression severity.

Late-night posting relates to poorer sleep quality.

Fewer online friends associate with lower survey-reported social connections.

Abstract

Digital traces are often used as a substitute for survey data. However, it is unclear whether and how digital traces actually correspond to the survey-based traits they purport to measure. This paper examines correlations between self-reports and digital trace proxies of depression, anxiety, mood, social integration and sleep among high school students. The study is based on a small but rich multilayer data set (N = 144). The data set contains mood and sleep measures, assessed daily over a 4-month period, along with survey measures at two points in time and information about online activity from VK, the most popular social networking site in Russia. Our analysis indicates that 1) the sentiments expressed in social media posts are correlated with depression; namely, adolescents with more severe symptoms of depression write more negative posts, 2) late-night posting indicates less sleep and poorer sleep quality, and 3) students who were nominated less often as somebody's friend in the survey have fewer friends on VK and their posts receive fewer "likes." However, these correlations are generally weak. These results demonstrate that digital traces can serve as useful supplements to, rather than substitutes for, survey data in studies on adolescents' well-being. These estimates of correlations between survey and digital trace data could provide useful guidelines for future research on the topic.