# Navigating online emotion: affective patterns and depressive traits in youth digital engagement

## TL;DR

This study explores how young people express emotions online and how this relates to depressive traits, finding that those with depressive tendencies show distinct emotional patterns.

## Contribution

The study introduces an integrative framework combining emotional models and thematic analysis to explore youth digital emotional expression.

## Key findings

- Youth online emotional expression is characterized by self-focus, high pleasure, and high arousal.
- Individuals with depressive tendencies show higher emotional arousal and more no-trigger expression.
- No-trigger expression mediates emotional expression mechanisms in youth with depressive traits.

## Abstract

Youth digital engagement serves as a notable avenue for the expression of emotion and the construction of self among today’s youth. This study aims to examine the patterns of youth online emotional expression and their association with individual psychological traits, particularly depressive tendencies.

23,966 Weibo posts published by 103 active youth users were sampled and analyzed. An integrative framework combining Russell’s Circumplex Model with multi-level thematic analysis was applied to code each post for valence, arousal, trigger type and coping strategy. Youths also completed a standard depression-screening scale; scores were used to contrast high- versus low-depressive trait sub-groups.

The findings reveal that youth online emotional expression overall is characterized by a self-focused nature, high pleasure, and high arousal. The study also found that individual psychological traits influence emotional expression patterns. Individuals with depressive tendencies showed a significant propensity for higher emotional arousal expression and more no-trigger expression. Furthermore, no-trigger expression plays a mediating role in their emotional expression mechanism.

The study provides an integrative framework for youth digital engagement and highlights “no-trigger” expression as a mediator in the framework. These findings can guide early detection efforts and contribute to designing targeted digital mental health supports, as well as informing guidance for families and platform managers.