KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text

TL;DR

This paper presents a method for detecting depression signs in social media text using linguistic features and interpretable models, achieving moderate performance and providing insights into model coefficients.

Contribution

It introduces a feature-based approach combining linguistic and readability measures with logistic regression for depression detection in social media posts.

Findings

Macro F1-score of 0.439 achieved

Ranked 25th out of 31 teams

Model interpretability aids future research

Abstract

In this paper we present our approach for detecting signs of depression from social media text. Our model relies on word unigrams, part-of-speech tags, readabilitiy measures and the use of first, second or third person and the number of words. Our best model obtained a macro F1-score of 0.439 and ranked 25th, out of 31 teams. We further take advantage of the interpretability of the Logistic Regression model and we make an attempt to interpret the model coefficients with the hope that these will be useful for further research on the topic.