Depression detection in social media posts using transformer-based models and auxiliary features

TL;DR

This paper introduces a transformer-based neural network model with auxiliary features and data augmentation techniques to improve depression detection accuracy in social media posts, achieving high precision, recall, and F1-scores.

Contribution

It presents a novel combination of DistilBERT, auxiliary metadata, linguistic markers, and data augmentation to enhance depression detection in social media texts.

Findings

Achieved weighted F1-score of 84.15%

Data augmentation improved F1-score from 72.59% to 84.15%

Model outperforms traditional machine learning approaches

Abstract

The detection of depression in social media posts is crucial due to the increasing prevalence of mental health issues. Traditional machine learning algorithms often fail to capture intricate textual patterns, limiting their effectiveness in identifying depression. Existing studies have explored various approaches to this problem but often fall short in terms of accuracy and robustness. To address these limitations, this research proposes a neural network architecture leveraging transformer-based models combined with metadata and linguistic markers. The study employs DistilBERT, extracting information from the last four layers of the transformer, applying learned weights, and averaging them to create a rich representation of the input text. This representation, augmented by metadata and linguistic markers, enhances the model's comprehension of each post. Dropout layers prevent overfitting, and a Multilayer Perceptron (MLP) is used for final classification. Data augmentation techniques, inspired by the Easy Data Augmentation (EDA) methods, are also employed to improve model performance. Using BERT, random insertion and substitution of phrases generate additional training data, focusing on balancing the dataset by augmenting underrepresented classes. The proposed model achieves weighted Precision, Recall, and F1-scores of 84.26%, 84.18%, and 84.15%, respectively. The augmentation techniques significantly enhance model performance, increasing the weighted F1-score from 72.59% to 84.15%.