Exploring Self-Identified Counseling Expertise in Online Support Forums

• URL: http://arxiv.org/abs/2106.12976v1
• Date: Thu, 24 Jun 2021 12:53:07 GMT
• Title: Exploring Self-Identified Counseling Expertise in Online Support Forums
• Authors: Allison Lahnala, Yuntian Zhao, Charles Welch, Jonathan K. Kummerfeld, Lawrence An, Kenneth Resnicow, Rada Mihalcea, Ver\'onica P\'erez-Rosas
• Abstract summary: We study the differences between interactions with peers and with self-identified mental health professionals. Our work contributes toward the developing efforts of understanding how health experts engage with health information- and support-seekers in social networks.
• Score: 26.086207762353336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A growing number of people engage in online health forums, making it important to understand the quality of the advice they receive. In this paper, we explore the role of expertise in responses provided to help-seeking posts regarding mental health. We study the differences between (1) interactions with peers; and (2) interactions with self-identified mental health professionals. First, we show that a classifier can distinguish between these two groups, indicating that their language use does in fact differ. To understand this difference, we perform several analyses addressing engagement aspects, including whether their comments engage the support-seeker further as well as linguistic aspects, such as dominant language and linguistic style matching. Our work contributes toward the developing efforts of understanding how health experts engage with health information- and support-seekers in social networks. More broadly, it is a step toward a deeper understanding of the styles of interactions that cultivate supportive engagement in online communities.

Related papers

• Social Support Detection from Social Media Texts [44.096359084699]
  Social support, conveyed through a multitude of interactions and platforms such as social media, plays a pivotal role in fostering a sense of belonging. This paper introduces Social Support Detection (SSD) as a Natural language processing (NLP) task aimed at identifying supportive interactions. We conducted experiments on a dataset comprising 10,000 YouTube comments.
  arXiv  Detail & Related papers  (2024-11-04T20:23:03Z)
• CBT-Bench: Evaluating Large Language Models on Assisting Cognitive Behavior Therapy [67.23830698947637]
  We propose a new benchmark, CBT-BENCH, for the systematic evaluation of cognitive behavioral therapy (CBT) assistance. We include three levels of tasks in CBT-BENCH: I: Basic CBT knowledge acquisition, with the task of multiple-choice questions; II: Cognitive model understanding, with the tasks of cognitive distortion classification, primary core belief classification, and fine-grained core belief classification; III: Therapeutic response generation, with the task of generating responses to patient speech in CBT therapy sessions. Experimental results indicate that while LLMs perform well in reciting CBT knowledge, they fall short in complex real-world scenarios
  arXiv  Detail & Related papers  (2024-10-17T04:52:57Z)
• How to Engage Your Readers? Generating Guiding Questions to Promote Active Reading [60.19226384241482]
  We introduce GuidingQ, a dataset of 10K in-text questions from textbooks and scientific articles. We explore various approaches to generate such questions using language models. We conduct a human study to understand the implication of such questions on reading comprehension.
  arXiv  Detail & Related papers  (2024-07-19T13:42:56Z)
• 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)
• The Role of Emotions in Informational Support Question-Response Pairs in Online Health Communities: A Multimodal Deep Learning Approach [0.0]
  This study explores the relationship between informational support seeking questions, responses, and helpfulness ratings in online health communities. We created a labeled data set of question-response pairs and developed multimodal machine learning and deep learning models to reliably predict informational support questions and responses. We employed explainable AI to reveal the emotions embedded in informational support exchanges, demonstrating the importance of emotion in providing informational support.
  arXiv  Detail & Related papers  (2024-05-21T15:15:08Z)
• K-ESConv: Knowledge Injection for Emotional Support Dialogue Systems via Prompt Learning [83.19215082550163]
  We propose K-ESConv, a novel prompt learning based knowledge injection method for emotional support dialogue system. We evaluate our model on an emotional support dataset ESConv, where the model retrieves and incorporates knowledge from external professional emotional Q&A forum.
  arXiv  Detail & Related papers  (2023-12-16T08:10:10Z)
• An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives [7.564560899044939]
  We conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques.
  arXiv  Detail & Related papers  (2023-10-25T21:37:57Z)
• Critical Behavioral Traits Foster Peer Engagement in Online Mental Health Communities [28.17719749654601]
  We introduce BeCOPE, a novel behavior encoded Peer counseling dataset comprising over 10,118 posts and 58,279 comments sourced from 21 mental health-specific subreddits. Our analysis indicates the prominence of self-criticism'' as the most prevalent form of criticism expressed by help-seekers, accounting for a significant 43% of interactions. We highlight the pivotal role of well-articulated problem descriptions, showing that superior readability effectively doubles the likelihood of receiving the sought-after support.
  arXiv  Detail & Related papers  (2023-09-04T14:00:12Z)
• 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)
• E-ffective: A Visual Analytic System for Exploring the Emotion and Effectiveness of Inspirational Speeches [57.279044079196105]
  E-ffective is a visual analytic system allowing speaking experts and novices to analyze both the role of speech factors and their contribution in effective speeches. Two novel visualizations include E-spiral (that shows the emotional shifts in speeches in a visually compact way) and E-script (that connects speech content with key speech delivery information.
  arXiv  Detail & Related papers  (2021-10-28T06:14:27Z)
• Assessing the Severity of Health States based on Social Media Posts [62.52087340582502]
  We propose a multiview learning framework that models both the textual content as well as contextual-information to assess the severity of the user's health state. The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.
  arXiv  Detail & Related papers  (2020-09-21T03:45:14Z)

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.