DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities

• URL: http://arxiv.org/abs/2210.05372v1
• Date: Mon, 10 Oct 2022 08:23:57 GMT
• Title: DEPTWEET: A Typology for Social Media Texts to Detect Depression Severities
• Authors: Mohsinul Kabir, Tasnim Ahmed, Md. Bakhtiar Hasan, Md Tahmid Rahman Laskar, Tarun Kumar Joarder, Hasan Mahmud, Kamrul Hasan
• Abstract summary: We leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) We present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as 'non-depressed' or 'depressed'
• Score: 0.46796109436086664
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Mental health research through data-driven methods has been hindered by a lack of standard typology and scarcity of adequate data. In this study, we leverage the clinical articulation of depression to build a typology for social media texts for detecting the severity of depression. It emulates the standard clinical assessment procedure Diagnostic and Statistical Manual of Mental Disorders (DSM-5) and Patient Health Questionnaire (PHQ-9) to encompass subtle indications of depressive disorders from tweets. Along with the typology, we present a new dataset of 40191 tweets labeled by expert annotators. Each tweet is labeled as 'non-depressed' or 'depressed'. Moreover, three severity levels are considered for 'depressed' tweets: (1) mild, (2) moderate, and (3) severe. An associated confidence score is provided with each label to validate the quality of annotation. We examine the quality of the dataset via representing summary statistics while setting strong baseline results using attention-based models like BERT and DistilBERT. Finally, we extensively address the limitations of the study to provide directions for further research.

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)
• A BERT-Based Summarization approach for depression detection [1.7363112470483526]
  Depression is a globally prevalent mental disorder with potentially severe repercussions if not addressed. Machine learning and artificial intelligence can autonomously detect depression indicators from diverse data sources. Our study proposes text summarization as a preprocessing technique to diminish the length and intricacies of input texts.
  arXiv  Detail & Related papers  (2024-09-13T02:14:34Z)
• LLM Questionnaire Completion for Automatic Psychiatric Assessment [49.1574468325115]
  We employ a Large Language Model (LLM) to convert unstructured psychological interviews into structured questionnaires spanning various psychiatric and personality domains. The obtained answers are coded as features, which are used to predict standardized psychiatric measures of depression (PHQ-8) and PTSD (PCL-C)
  arXiv  Detail & Related papers  (2024-06-09T09:03:11Z)
• Your Model Is Not Predicting Depression Well And That Is Why: A Case Study of PRIMATE Dataset [0.0]
  This paper addresses the quality of annotations in mental health datasets used for NLP-based depression level estimation from social media texts. Our study reveals concerns regarding annotation validity, particularly for the lack of interest or pleasure symptom. Our refined annotations, to be released under a Data Use Agreement, offer a higher-quality test set for anhedonia detection.
  arXiv  Detail & Related papers  (2024-03-01T10:47:02Z)
• What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media [0.5156484100374058]
  Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media.
  arXiv  Detail & Related papers  (2023-05-18T20:15:04Z)
• Depression detection in social media posts using affective and social norm features [84.12658971655253]
  We propose a deep architecture for depression detection from social media posts. We incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. The inclusion of the proposed features yields state-of-the-art results in both settings.
  arXiv  Detail & Related papers  (2023-03-24T21:26:27Z)
• Handwriting and Drawing for Depression Detection: A Preliminary Study [53.11777541341063]
  Short-term covid effects on mental health were a significant increase in anxiety and depressive symptoms. The aim of this study is to use a new tool, the online handwriting and drawing analysis, to discriminate between healthy individuals and depressed patients.
  arXiv  Detail & Related papers  (2023-02-05T22:33:49Z)
• 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)
• DECK: Behavioral Tests to Improve Interpretability and Generalizability of BERT Models Detecting Depression from Text [4.269268432906194]
  Models that accurately detect depression from text are important tools for addressing the post-pandemic mental health crisis. BERT-based classifiers' promising performance and the off-the-shelf availability make them great candidates for this task. We introduce the DECK (DEpression ChecKlist), depression-specific model behavioural tests that allow better interpretability.
  arXiv  Detail & Related papers  (2022-09-12T14:39:46Z)
• Data set creation and empirical analysis for detecting signs of depression from social media postings [0.0]
  Depression is a common mental illness that has to be detected and treated at an early stage to avoid serious consequences. We developed a gold standard data set that detects the levels of depression as not depressed', moderately depressed' and severely depressed' from the social media postings.
  arXiv  Detail & Related papers  (2022-02-07T10:24:33Z)
• 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)

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.