Chatbots for Mental Health Support: Exploring the Impact of Emohaa on
Reducing Mental Distress in China
- URL: http://arxiv.org/abs/2209.10183v1
- Date: Wed, 21 Sep 2022 08:23:40 GMT
- Title: Chatbots for Mental Health Support: Exploring the Impact of Emohaa on
Reducing Mental Distress in China
- Authors: Sahand Sabour, Wen Zhang, Xiyao Xiao, Yuwei Zhang, Yinhe Zheng, Jiaxin
Wen, Jialu Zhao, Minlie Huang
- Abstract summary: The study included 134 participants, split into three groups: Emohaa (CBT-based), Emohaa (Full) and control.
Emohaa is a conversational agent that provides cognitive support through CBT-based exercises and guided conversations.
It also emotionally supports users by enabling them to vent their desired emotional problems.
- Score: 50.12173157902495
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The growing demand for mental health support has highlighted the importance
of conversational agents as human supporters worldwide and in China. These
agents could increase availability and reduce the relative costs of mental
health support. The provided support can be divided into two main types:
cognitive and emotional support. Existing work on this topic mainly focuses on
constructing agents that adopt Cognitive Behavioral Therapy (CBT) principles.
Such agents operate based on pre-defined templates and exercises to provide
cognitive support. However, research on emotional support using such agents is
limited. In addition, most of the constructed agents operate in English,
highlighting the importance of conducting such studies in China. In this study,
we analyze the effectiveness of Emohaa in reducing symptoms of mental distress.
Emohaa is a conversational agent that provides cognitive support through
CBT-based exercises and guided conversations. It also emotionally supports
users by enabling them to vent their desired emotional problems. The study
included 134 participants, split into three groups: Emohaa (CBT-based), Emohaa
(Full), and control. Experimental results demonstrated that compared to the
control group, participants who used Emohaa experienced considerably more
significant improvements in symptoms of mental distress. We also found that
adding the emotional support agent had a complementary effect on such
improvements, mainly depression and insomnia. Based on the obtained results and
participants' satisfaction with the platform, we concluded that Emohaa is a
practical and effective tool for reducing mental distress.
Related papers
- Towards Understanding Emotions for Engaged Mental Health Conversations [1.3654846342364306]
We are developing a system to perform passive emotion-sensing using a combination of keystroke dynamics and sentiment analysis.
The analysis of short text messages and keyboard typing patterns can provide emotion information that may be used to support both clients and responders.
arXiv Detail & Related papers (2024-06-17T01:27:15Z) - LLM Agents for Psychology: A Study on Gamified Assessments [71.08193163042107]
Psychological measurement is essential for mental health, self-understanding, and personal development.
PsychoGAT (Psychological Game AgenTs) achieves statistically significant excellence in psychometric metrics such as reliability, convergent validity, and discriminant validity.
arXiv Detail & Related papers (2024-02-19T18:00:30Z) - CauESC: A Causal Aware Model for Emotional Support Conversation [79.4451588204647]
Existing approaches ignore the emotion causes of the distress.
They focus on the seeker's own mental state rather than the emotional dynamics during interaction between speakers.
We propose a novel framework CauESC, which firstly recognizes the emotion causes of the distress, as well as the emotion effects triggered by the causes.
arXiv Detail & Related papers (2024-01-31T11:30:24Z) - Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring [8.806947407907137]
We study how human-language model interaction can support self-guided mental health interventions.
We design and evaluate a system that uses language models to support people through various steps of cognitive restructuring.
arXiv Detail & Related papers (2023-10-24T02:23:34Z) - Large Language Models Understand and Can be Enhanced by Emotional
Stimuli [53.53886609012119]
We take the first step towards exploring the ability of Large Language Models to understand emotional stimuli.
Our experiments show that LLMs have a grasp of emotional intelligence, and their performance can be improved with emotional prompts.
Our human study results demonstrate that EmotionPrompt significantly boosts the performance of generative tasks.
arXiv Detail & Related papers (2023-07-14T00:57:12Z) - PAL: Persona-Augmented Emotional Support Conversation Generation [54.069321178816686]
Due to the lack of human resources for mental health support, there is an increasing demand for employing conversational agents for support.
Recent work has demonstrated the effectiveness of dialogue models in providing emotional support.
We propose a framework for dynamically inferring and modeling seekers' persona.
arXiv Detail & Related papers (2022-12-19T04:12:54Z) - Exploring the Effects of AI-assisted Emotional Support Processes in
Online Mental Health Community [26.36961585672868]
We design an AI-infused workflow that allows users to write emotional supporting messages to other users' posts.
Based on a preliminary user study, we identified that the system helped seekers to clarify emotion and describe text concretely.
arXiv Detail & Related papers (2022-02-21T09:25:36Z) - Towards Emotional Support Dialog Systems [61.58828606097423]
We define the Emotional Support Conversation task and propose an ESC Framework, which is grounded on the Helping Skills Theory.
We construct an Emotion Support Conversation dataset (ESConv) with rich annotation (especially support strategy) in a help-seeker and supporter mode.
We evaluate state-of-the-art dialog models with respect to the ability to provide emotional support.
arXiv Detail & Related papers (2021-06-02T13:30:43Z) - Predicting User Emotional Tone in Mental Disorder Online Communities [2.365702128814616]
We analyze how discussions in Reddit communities related to mental disorders can help improve the health conditions of their users.
Using the emotional tone of users' writing as a proxy for emotional state, we uncover relationships between user interactions and state changes.
We build models based on SOTA text embedding techniques and RNNs to predict shifts in emotional tone.
arXiv Detail & Related papers (2020-05-15T11:25:08Z)
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.