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

arXiv: https://arxiv.org/abs/2204.04481 · HTML: https://mlsanity.com/paper/2204.04481/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 9 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Data set creation and empirical analysis for detecting signs of depression from social media postings](https://mlsanity.com/paper/2202.03047/) (2022)
- [Not Just Depressed: Bipolar Disorder Prediction on Reddit](https://mlsanity.com/paper/1811.04655/) (2018)

## Built on by (most cited first, 1 of 1)

- [On-device Federated Learning in Smartphones for Detecting Depression from Reddit Posts](https://mlsanity.com/paper/2410.13709/) (2024)

## Abstract (arXiv)

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.