A Psychologically Informed Part-of-Speech Analysis of Depression in Social Media

TL;DR

This study analyzes social media language patterns of depressed users through part-of-speech features, confirming psychological theories and aiding in mental health monitoring.

Contribution

It introduces a large-scale, computational part-of-speech analysis of social media discourse of depressed individuals, linking linguistic features to psychological insights.

Findings

Depressed users show more self-focus in language.

Statistically significant differences in part-of-speech usage between depressed and non-depressed.

Provides data-driven insights for mental health monitoring tools.

Abstract

In this work, we provide an extensive part-of-speech analysis of the discourse of social media users with depression. Research in psychology revealed that depressed users tend to be self-focused, more preoccupied with themselves and ruminate more about their lives and emotions. Our work aims to make use of large-scale datasets and computational methods for a quantitative exploration of discourse. We use the publicly available depression dataset from the Early Risk Prediction on the Internet Workshop (eRisk) 2018 and extract part-of-speech features and several indices based on them. Our results reveal statistically significant differences between the depressed and non-depressed individuals confirming findings from the existing psychology literature. Our work provides insights regarding the way in which depressed individuals are expressing themselves on social media platforms, allowing for better-informed computational models to help monitor and prevent mental illnesses.