Work

The Visibility of Depression in Social Media: Mapping Symptoms to Linguistic Features

Year: 2026

Type: conference-paper

Abstract: Digital phenotyping research assumes that depression symptoms are detectable in people's written discourse, yet there is room to explore which specific symptoms leave linguistic traces and which remain invisible.In this paper, using matched clinical and social media data from 169 Reddit users (eRisk 2021), we construct a clinical symptom network from BDI-II responses and a symptom-language bridge matrix mapping each of the 21 BDI-II symptoms to 15 curated LIWC-22 linguistic features.After FDR correction, 37 significant associations emerge, revealing a divide between cognitive-affective symptoms (sadness, worthlessness, suicidality) that leave clear linguistic traces through mental health vocabulary, anxiety words, and first-person pronouns, while others, like vegetative symptoms (sleep, appetite, irritability, libido) appear less visible as captured by the selected linguistic features.These findings suggest that there might be dimensions of depression that are missed by popular methods of text-based depression monitoring in social media data.

Source: Workshop on Computational Linguistics and Clinical Psychology (CLPsych)

DOI: 10.18653/v1/2026.clpsych-1.28

Authors: Ștefana-Arina Tăbușcă, Ana Sabina Uban, Liviu Dinu

Institution: University of Bucharest

Language: English

Topic: Mental Health via Writing

Subfield: Social Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funder: Health Resources in Action