DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media

Aragón, Mario Ezra; Monroy, Adrián Pastor López; González, Luis; Losada, David E.; Montes, Manuel

doi:10.18653/v1/2023.acl-long.853

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other2

Book1

Article1

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

TVD-BERT: A Domain-Adaptation Pre-trained Model for Textural Vulnerability Descriptions

Wang,

Liang,

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

TVD-BERT: A Domain-Adaptation Pre-trained Model for Textural Vulnerability Descriptions

Wang,

Liang,

et al. 2024

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Explainable depression symptom detection in social media

Bao,

Pérez,

Parapar

2024

Health Inf Sci Syst

View full text Add to dashboard Cite

Users of social platforms often perceive these sites as supportive spaces to post about their mental health issues. Those conversations contain important traces about individuals’ health risks. Recently, researchers have exploited this online information to construct mental health detection models, which aim to identify users at risk on platforms like Twitter, Reddit or Facebook. Most of these models are focused on achieving good classification results, ignoring the explainability and interpretability of the decisions. Recent research has pointed out the importance of using clinical markers, such as the use of symptoms, to improve trust in the computational models by health professionals. In this paper, we introduce transformer-based architectures designed to detect and explain the appearance of depressive symptom markers in user-generated content from social media. We present two approaches: (i) train a model to classify, and another one to explain the classifier’s decision separately and (ii) unify the two tasks simultaneously within a single model. Additionally, for this latter manner, we also investigated the performance of recent conversational Large Language Models (LLMs) utilizing both in-context learning and finetuning. Our models provide natural language explanations, aligning with validated symptoms, thus enabling clinicians to interpret the decisions more effectively. We evaluate our approaches using recent symptom-focused datasets, using both offline metrics and expert-in-the-loop evaluations to assess the quality of our models’ explanations. Our findings demonstrate that it is possible to achieve good classification results while generating interpretable symptom-based explanations.

show abstract

A Review of Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches

Saleem,

Afzal

2024

2024 International Conference on Engineering &Amp;amp; Computing Technologies (ICECT)

View full text Add to dashboard Cite

DisorBERT: A Double Domain Adaptation Model for Detecting Signs of Mental Disorders in Social Media

Cited by 4 publications

References 0 publications

TVD-BERT: A Domain-Adaptation Pre-trained Model for Textural Vulnerability Descriptions

TVD-BERT: A Domain-Adaptation Pre-trained Model for Textural Vulnerability Descriptions

Explainable depression symptom detection in social media

A Review of Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches

Contact Info

Product

Resources

About