UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for
Classifying Common Mental Illnesses on Social Media Posts
- URL: http://arxiv.org/abs/2304.04539v1
- Date: Mon, 10 Apr 2023 12:18:53 GMT
- Title: UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensemble of BERTs for
Classifying Common Mental Illnesses on Social Media Posts
- Authors: Pratinav Seth and Mihir Agarwal
- Abstract summary: We propose UATTA-EB: Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing reliable and well-calibrated predictions.
We analyze unstructured user data on Reddit to classify six possible types of mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Given the current state of the world, because of existing situations around
the world, millions of people suffering from mental illnesses feel isolated and
unable to receive help in person. Psychological studies have shown that our
state of mind can manifest itself in the linguistic features we use to
communicate. People have increasingly turned to online platforms to express
themselves and seek help with their conditions. Deep learning methods have been
commonly used to identify and analyze mental health conditions from various
sources of information, including social media. Still, they face challenges,
including a lack of reliability and overconfidence in predictions resulting in
the poor calibration of the models. To solve these issues, We propose UATTA-EB:
Uncertainty-Aware Test-Time Augmented Ensembling of BERTs for producing
reliable and well-calibrated predictions to classify six possible types of
mental illnesses- None, Depression, Anxiety, Bipolar Disorder, ADHD, and PTSD
by analyzing unstructured user data on Reddit.
Related papers
- MentalArena: Self-play Training of Language Models for Diagnosis and Treatment of Mental Health Disorders [59.515827458631975]
Mental health disorders are one of the most serious diseases in the world.
Privacy concerns limit the accessibility of personalized treatment data.
MentalArena is a self-play framework to train language models.
arXiv Detail & Related papers (2024-10-09T13:06:40Z) - Empowering Psychotherapy with Large Language Models: Cognitive
Distortion Detection through Diagnosis of Thought Prompting [82.64015366154884]
We study the task of cognitive distortion detection and propose the Diagnosis of Thought (DoT) prompting.
DoT performs diagnosis on the patient's speech via three stages: subjectivity assessment to separate the facts and the thoughts; contrastive reasoning to elicit the reasoning processes supporting and contradicting the thoughts; and schema analysis to summarize the cognition schemas.
Experiments demonstrate that DoT obtains significant improvements over ChatGPT for cognitive distortion detection, while generating high-quality rationales approved by human experts.
arXiv Detail & Related papers (2023-10-11T02:47:21Z) - LOST: A Mental Health Dataset of Low Self-esteem in Reddit Posts [4.6071451559137175]
Low self-esteem and interpersonal needs have a major impact on depression and suicide attempts.
Individuals seek social connectedness on social media to boost and alleviate their loneliness.
We introduce a psychology-grounded and expertly annotated dataset, LoST: Low Self esTeem, to study and detect low self-esteem on Reddit.
arXiv Detail & Related papers (2023-06-08T23:52:35Z) - Semantic Similarity Models for Depression Severity Estimation [53.72188878602294]
This paper presents an efficient semantic pipeline to study depression severity in individuals based on their social media writings.
We use test user sentences for producing semantic rankings over an index of representative training sentences corresponding to depressive symptoms and severity levels.
We evaluate our methods on two Reddit-based benchmarks, achieving 30% improvement over state of the art in terms of measuring depression severity.
arXiv Detail & Related papers (2022-11-14T18:47:26Z) - Towards Knowledge-based Mining of Mental Disorder Patterns from Textual
Data [0.0]
Mental health disorders may cause severe consequences on all the countries' economies and health.
Identifying early signs of mental health disorders is vital.
For example, depression may increase an individual's risk of suicide.
arXiv Detail & Related papers (2022-07-07T10:04:43Z) - Mental Illness Classification on Social Media Texts using Deep Learning
and Transfer Learning [55.653944436488786]
According to the World health organization (WHO), approximately 450 million people are affected.
Mental illnesses, such as depression, anxiety, bipolar disorder, ADHD, and PTSD.
This study analyzes unstructured user data on Reddit platform and classifies five common mental illnesses: depression, anxiety, bipolar disorder, ADHD, and PTSD.
arXiv Detail & Related papers (2022-07-03T11:33:52Z) - Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed
Depression Diagnoses [15.002282686061905]
We ask: to what extent are self-disclosures of mental health diagnoses actually relevant over time?
We analyze recent activity from individuals who disclosed a depression diagnosis on social media over five years ago.
We provide expanded evidence for the presence of personality-related biases in datasets curated using self-disclosed diagnoses.
arXiv Detail & Related papers (2022-06-22T15:02:03Z) - Deep Multi-task Learning for Depression Detection and Prediction in
Longitudinal Data [50.02223091927777]
Depression is among the most prevalent mental disorders, affecting millions of people of all ages globally.
Machine learning techniques have shown effective in enabling automated detection and prediction of depression for early intervention and treatment.
We introduce a novel deep multi-task recurrent neural network to tackle this challenge, in which depression classification is jointly optimized with two auxiliary tasks.
arXiv Detail & Related papers (2020-12-05T05:14:14Z) - Detection and Classification of mental illnesses on social media using
RoBERTa [0.3753841394482697]
In this work, we detect and classify five prominent kinds of mental illnesses: depression, anxiety, bipolar disorder, ADHD and PTSD.
We believe that our work is the first multi-class model that uses a Transformer-based architecture such as RoBERTa to analyze people's emotions and psychology.
arXiv Detail & Related papers (2020-11-23T05:54:46Z) - Anxiety Detection Leveraging Mobile Passive Sensing [53.11661460916551]
Anxiety disorders are the most common class of psychiatric problems affecting both children and adults.
Leveraging passive and unobtrusive data collection from smartphones could be a viable alternative to classical methods.
eWellness is an experimental mobile application designed to track a full-suite of sensor and user-log data off an individual's device in a continuous and passive manner.
arXiv Detail & Related papers (2020-08-09T20:22:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.