Previously submitted to: JMIR Mental Health (no longer under consideration since Dec 07, 2023)
Date Submitted: Dec 5, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
HelaDepDet 2.0 - A Novel Deep Learning Approach for Detecting the Severity of Human Depression on Social Media Analysis
ABSTRACT
Background:
Depression affects the mental and physical well-being of humans. According to the latest statistics, cases of depression-driven self-harm and suicide are reported at an alarming rate. Although multiple clinical diagnosis methods have been established, the requirements of having skilled medical staff, equipment, and processing time limit their accessibility. Depression detection using social media data has therefore become a growing research area. Social media is used to express emotions and feelings hence analyzing such content could help to detect abnormalities sooner than by clinical diagnosis. Even though there are systems which can detect the presence or absence of depression, such methods cannot determine the severity of depression and many of them were not validated on large corpora.
Objective:
In this study, we propose a confidence vector based novel deep learning approach for detecting the severity of human depression. A well-balanced and aggregated novel dataset is also introduced as a part of this study to validate our proposed model on a large corpus.
Methods:
We formulated the problem of interest into a multiclass classification problem. The core logic is to generate confidence vectors considering the identified depressive words/phrases of each extracted social media statements and then to obtain the exact depression severity level using a fully connected deep neural network. To validate our method, we aggregated two labelled, unbalanced, and small public corpora for depression severity detection to obtain a well-balanced dataset consisting of more than 40,000 social media statements. Optimal data balancing techniques were applied to maintain a fair number of samples in each depression severity class. To our knowledge, this is the largest corpus for depression severity detection to date.
Results:
Experimental results showed that our methodology achieved a new state-of-the-art (SOTA) performance in depression severity detection with Precision, Recall, and F1 Scores of 79%, 77%, and 76%, respectively, over existing baselines.
Conclusions:
The results showed that our method is competitive with baseline models for depression severity detection.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.