# Semantic Agency Patterns Signal Depressive Experiences: Evidence From Postpartum Communication on Social Media

## TL;DR

This study shows that language reflecting a lack of agency on social media can indicate depressive experiences, especially in postpartum individuals.

## Contribution

The paper introduces semantic agency as a novel linguistic marker for detecting depressive experiences in postpartum communication.

## Key findings

- Reduced semantic agency consistently indicates depressive features in social media posts.
- Semantic agency negatively correlates with established linguistic markers of depression.
- Lower semantic agency is linked to depressive experiences as coded by experts.

## Abstract

Depression‐related symptoms, such as loss of motivation and diminished interest in activities, correspond to loss of agency. Given recent evidence that agency (or its lack) can be reliably detected in language, we investigated how linguistic manifestations of agency relate to depressive experiences. In two studies, we explored whether semantic agency can serve as a novel marker of depressive experiences within the context of postpartum. We analyzed data from Twitter (Study 1, N = 17,664 tweets) and Reddit (Study 2, N = 3033 posts), using three complementary approaches: machine learning‐based topic detection, analysis of established linguistic markers of depression, and expert coding of depressive experiences. Across both studies, reduced semantic agency consistently emerged as a reliable indicator of depressive features. Posts discussing individuals’ depressive experiences in the postpartum period exhibited lower levels of semantic agency; semantic agency within posts was negatively correlated with established linguistic markers of depression; and semantic agency was negatively linked to depressive experiences as coded by experts. These findings highlight the potential of semantic analysis for mental health applications, suggesting that agency‐based markers could enrich existing linguistic frameworks examining psychological distress. While this research is at an early stage, future validation could clarify whether such markers might enhance the sensitivity of language‐based screening tools for identifying individuals in need of mental health support.