The Semantics of Depression: How Linguistic Agency Patterns Signal Depressive Symptoms on Social Media

Depression-related symptoms, such as a loss of motivation and diminished interest in activities, correspond to a loss of agency. Given that recent research demonstrates that agency (or its lack) can be reliably detected in language, we investigated how linguistic manifestations of agency link to depressive symptoms. In two studies, we explored whether agency in language can serve as a novel marker of depressive symptoms, within the context of postpartum. We analysed data from Twitter (Study 1, N = 17,664 tweets) and Reddit (Study 2, N= 3,033 posts), using three approaches: machine learning-based topic detection, analysis of established linguistic markers of depression, and expert coding of depressive symptoms. Across both studies, reduced semantic agency consistently emerged as a reliable indicator of depressive symptoms. Posts discussing postpartum depression exhibited lower levels of semantic agency; semantic agency within posts was negatively correlated with other established markers of depression; and semantic agency was negatively linked to depressive symptoms as coded by experts. This research enhances our understanding of the cognitive aspects of depression and highlights the value of semantic analysis for mental health applications. Integrating agency-based markers into existing linguistic frameworks has the potential to improve the accuracy of language-based depression screening algorithms. More sensitive algorithms could better identify individuals in need of support, paving the way for effective mental health interventions.