DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text

Eduardo Garcia, Juliana Gomes, Adalberto Barbosa Júnior, Cardeque Borges, Nádia da Silva
11/8/2023
cs.CLcs.AI

No official code found. Best implementation: eduagarcia/depsign-2023-ranlp (3 stars, last commit 2024-02-16).

Abstract

In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023, achieving a 47.0% Macro F1-Score and a notable 2.4% advantage. The task was to classify social media texts into three distinct levels of depression - "not depressed," "moderately depressed," and "severely depressed." Leveraging the power of the RoBERTa and DeBERTa models, we further pre-trained them on a collected Reddit dataset, specifically curated from mental health-related Reddit's communities (Subreddits), leading to an enhanced understanding of nuanced mental health discourse. To address lengthy textual data, we used truncation techniques that retained the essence of the content by focusing on its beginnings and endings. Our model was robust against unbalanced data by incorporating sample weights into the loss. Cross-validation and ensemble techniques were then employed to combine our k-fold trained models, delivering an optimal solution. The accompanying code is made available for transparency and further development.

View on arXivView PDF

Code Implementations(1)

eduagarcia/depsign-2023-ranlpCommunity100%

1st Place Solution of the Shared task on Detecting Signs of Depression from Social Media Text at LT-EDI@RANLP 2023

30Shell, PythonJun 12, 20232 years ago

Similar Papers

A Framework for Identifying Depression on Social Media: MentalRiskES@IberLEF 2023

90% match1 repo2023

MASON-NLP at eRisk 2023: Deep Learning-Based Detection of Depression Symptoms from Social Media Texts

88% match2023

Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks

87% match2023

SERCNN: Stacked Embedding Recurrent Convolutional Neural Network in Detecting Depression on Twitter

87% match2022

Calibration of Transformer-based Models for Identifying Stress and Depression in Social Media

86% match2023

Utilizing ChatGPT Generated Data to Retrieve Depression Symptoms from Social Media

86% match2023

Cite this paper

@article{garcia2023deeplearningbrasilltedi,
  title  = {DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text},
  author = {Eduardo Garcia and Juliana Gomes and Adalberto Barbosa Júnior and Cardeque Borges and Nádia da Silva},
  year   = {2023},
  eprint = {2311.05047},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2311.05047v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2311.05047.svg)](https://codeofpaper.com/paper/2311.05047)

Discussion