Depression is a global burden and one of the most challenging mental health
conditions to control. Experts can detect its severity early using the Beck
Depression Inventory (BDI) questionnaire, administer appropriate medication to
patients, and impede its progression. Due to the fear of potential
stigmatization, many patients turn to social media platforms like Reddit for
advice and assistance at various stages of their journey. This research
extracts text from Reddit to facilitate the diagnostic process. It employs a
proposed labeling approach to categorize the text and subsequently fine-tunes
the Longformer model. The model's performance is compared against baseline
models, including Naive Bayes, Random Forest, Support Vector Machines, and
Gradient Boosting. Our findings reveal that the Longformer model outperforms
the baseline models in both English (48%) and Luganda (45%) languages on a
custom-made dataset.

本研究使用来自 Reddit 的文本，提出了一种标记方法对文本进行分类，并对 Longformer 模型进行微调。研究发现，在自制数据集上，Longformer 模型在英语（48%）和卢干达语（45%）中均表现优于基准模型，对抑郁症的早期诊断具有较好的效果。

Reddit 文本增强标记技术与 Fine-Tuned 长模型在英语和卢干达中用于分类抑郁症严重程度

Enhanced Labeling Technique for Reddit Text and Fine-Tuned Longformer  Models for Classifying Depression Severity in English and Luganda

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.

通过社交媒体和深度学习方法研究抑郁症症状语句的鉴别和相关性评估。

MASON-NLP 参加 eRisk 2023：基于深度学习的社交媒体文本抑郁症状检测

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

Depressive disorders constitute a severe public health issue worldwide.
However, public health systems have limited capacity for case detection and
diagnosis. In this regard, the widespread use of social media has opened up a
way to access public information on a large scale. Computational methods can
serve as support tools for rapid screening by exploiting this user-generated
social media content. This paper presents an efficient semantic pipeline to
study depression severity in individuals based on their social media writings.
We select test user sentences for producing semantic rankings over an index of
representative training sentences corresponding to depressive symptoms and
severity levels. Then, we use the sentences from those results as evidence for
predicting users' symptom severity. For that, we explore different aggregation
methods to answer one of four Beck Depression Inventory (BDI) options per
symptom. We evaluate our methods on two Reddit-based benchmarks, achieving 30\%
improvement over state of the art in terms of measuring depression severity.

本文提出了一个有效的语义管道，基于社交媒体的写作，研究个体的抑郁症严重程度。通过计算方法，利用用户生成的社交媒体内容，本文探讨了不同聚合方法以准确预测用户症状严重程度，并在 Reddit 的两个基准测试中取得了 30％的提高。