Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings

• URL: http://arxiv.org/abs/2209.07494v1
• Date: Thu, 15 Sep 2022 17:36:18 GMT
• Title: Hierarchical Attention Network for Explainable Depression Detection on Twitter Aided by Metaphor Concept Mappings
• Authors: Sooji Han, Rui Mao, and Erik Cambria
• Abstract summary: We propose a novel explainable model for depression detection on Twitter. It comprises a novel encoder combining hierarchical attention mechanisms and feed-forward neural networks. It not only detects depressed individuals, but also identifies features of such users' tweets and associated metaphor concept mappings.
• Score: 15.19024278125422
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic depression detection on Twitter can help individuals privately and conveniently understand their mental health status in the early stages before seeing mental health professionals. Most existing black-box-like deep learning methods for depression detection largely focused on improving classification performance. However, explaining model decisions is imperative in health research because decision-making can often be high-stakes and life-and-death. Reliable automatic diagnosis of mental health problems including depression should be supported by credible explanations justifying models' predictions. In this work, we propose a novel explainable model for depression detection on Twitter. It comprises a novel encoder combining hierarchical attention mechanisms and feed-forward neural networks. To support psycholinguistic studies, our model leverages metaphorical concept mappings as input. Thus, it not only detects depressed individuals, but also identifies features of such users' tweets and associated metaphor concept mappings.

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)
• They Look Like Each Other: Case-based Reasoning for Explainable Depression Detection on Twitter using Large Language Models [3.5904920375592098]
  We introduce ProtoDep, a novel, explainable framework for Twitter-based depression detection. ProtoDep provides transparent explanations at three levels: (i) symptom-level explanations for each tweet and user, (ii) case-based explanations comparing the user to similar individuals, and (iii) transparent decision-making through classification weights.
  arXiv  Detail & Related papers  (2024-07-21T20:13:50Z)
• Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data [2.76101452577748]
  The COVID-19 pandemic has escalated mental health crises worldwide. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression. As these conditions develop, signs of suicidal ideation may manifest in social media interactions.
  arXiv  Detail & Related papers  (2023-12-13T17:15:12Z)
• 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)
• Memory-Augmented Theory of Mind Network [59.9781556714202]
  Social reasoning requires the capacity of theory of mind (ToM) to contextualise and attribute mental states to others. Recent machine learning approaches to ToM have demonstrated that we can train the observer to read the past and present behaviours of other agents. We tackle the challenges by equipping the observer with novel neural memory mechanisms to encode, and hierarchical attention to selectively retrieve information about others. This results in ToMMY, a theory of mind model that learns to reason while making little assumptions about the underlying mental processes.
  arXiv  Detail & Related papers  (2023-01-17T14:48:58Z)
• NeuroExplainer: Fine-Grained Attention Decoding to Uncover Cortical Development Patterns of Preterm Infants [73.85768093666582]
  We propose an explainable geometric deep network dubbed NeuroExplainer. NeuroExplainer is used to uncover altered infant cortical development patterns associated with preterm birth.
  arXiv  Detail & Related papers  (2023-01-01T12:48:12Z)
• 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)
• 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)
• A Novel Sentiment Analysis Engine for Preliminary Depression Status Estimation on Social Media [0.0]
  We propose a cloud-based smartphone application, with a deep learning-based backend to primarily perform depression detection on Twitter social media. A psychologist could leverage the application to assess the patient's depression status prior to counseling, which provides better insight into the mental health status of a patient. The model achieved pinnacle results, with a testing accuracy of 87.23% and an AUC of 0.8621.
  arXiv  Detail & Related papers  (2020-11-29T04:42:53Z)
• Machine Learning-based Approach for Depression Detection in Twitter Using Content and Activity Features [0.0]
  Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets.
  arXiv  Detail & Related papers  (2020-03-09T11:27:39Z)

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.