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

TL;DR

This paper presents Mason-NLP's deep learning approach using MentalBERT, RoBERTa, and LSTM to identify sentences related to depression symptoms in social media texts, highlighting challenges and future prospects.

Contribution

The study applies a novel combination of deep learning models to detect depression symptoms in social media posts for the eRisk 2023 task.

Findings

Lower-than-expected evaluation results highlight challenges in ranking sentences.

Deep learning models can identify depression-related sentences but require further refinement.

Computational resources and methodological choices significantly impact performance.

Abstract

Depression is a mental health disorder that has a profound impact on people's lives. Recent research suggests that signs of depression can be detected in the way individuals communicate, both through spoken words and written texts. In particular, social media posts are a rich and convenient text source that we may examine for depressive symptoms. The Beck Depression Inventory (BDI) Questionnaire, which is frequently used to gauge the severity of depression, is one instrument that can aid in this study. We can narrow our study to only those symptoms since each BDI question is linked to a particular depressive symptom. It's important to remember that not everyone with depression exhibits all symptoms at once, but rather a combination of them. Therefore, it is extremely useful to be able to determine if a sentence or a piece of user-generated content is pertinent to a certain condition. With this in mind, the eRisk 2023 Task 1 was designed to do exactly that: assess the relevance of different sentences to the symptoms of depression as outlined in the BDI questionnaire. This report is all about how our team, Mason-NLP, participated in this subtask, which involved identifying sentences related to different depression symptoms. We used a deep learning approach that incorporated MentalBERT, RoBERTa, and LSTM. Despite our efforts, the evaluation results were lower than expected, underscoring the challenges inherent in ranking sentences from an extensive dataset about depression, which necessitates both appropriate methodological choices and significant computational resources. We anticipate that future iterations of this shared task will yield improved results as our understanding and techniques evolve.