@inproceedings{zhang-etal-2026-synthesizing,
title = "Synthesizing Dialogues from Social Media Data for Mental Health Support: A Case Study on Gambling Disorders",
author = "Zhang, Zhouqing and
Pereira, Lis and
She, Wan Jou and
Adams, Benjamin and
Piumsomboon, Tham and
Liew, Kongmeng",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.54/",
pages = "1055--1073",
ISBN = "979-8-89176-444-6",
abstract = "Recent advances in artificial intelligence have seen natural language processing (NLP) applications used increasingly for mental health support, such as question-answering systems, conversational agents, and chatbots. However, these applications often require high-quality dialogue datasets, which are scarce for certain under-resourced mental health conditions like gambling disorders. Privacy concerns and the sensitive nature of mental health issues make real-world data collection difficult and time-consuming. We propose a framework that uses publicly available social media posts as seeds to generate empathy-driven multi-turn dialogues between help-seekers and supporters in mental health conditions that have been under-researched in NLP. Through a case study on gambling disorder, we demonstrate the effectiveness of our approach in producing higher-quality dialogues than standard role-playing prompts. Our work showcases an effective dataset generation method for fine-tuning mental health language models alongside downstream applications like dialogue system development, particularly for low-resourced mental health disorders like gambling disorders."
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<abstract>Recent advances in artificial intelligence have seen natural language processing (NLP) applications used increasingly for mental health support, such as question-answering systems, conversational agents, and chatbots. However, these applications often require high-quality dialogue datasets, which are scarce for certain under-resourced mental health conditions like gambling disorders. Privacy concerns and the sensitive nature of mental health issues make real-world data collection difficult and time-consuming. We propose a framework that uses publicly available social media posts as seeds to generate empathy-driven multi-turn dialogues between help-seekers and supporters in mental health conditions that have been under-researched in NLP. Through a case study on gambling disorder, we demonstrate the effectiveness of our approach in producing higher-quality dialogues than standard role-playing prompts. Our work showcases an effective dataset generation method for fine-tuning mental health language models alongside downstream applications like dialogue system development, particularly for low-resourced mental health disorders like gambling disorders.</abstract>
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%0 Conference Proceedings
%T Synthesizing Dialogues from Social Media Data for Mental Health Support: A Case Study on Gambling Disorders
%A Zhang, Zhouqing
%A Pereira, Lis
%A She, Wan Jou
%A Adams, Benjamin
%A Piumsomboon, Tham
%A Liew, Kongmeng
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F zhang-etal-2026-synthesizing
%X Recent advances in artificial intelligence have seen natural language processing (NLP) applications used increasingly for mental health support, such as question-answering systems, conversational agents, and chatbots. However, these applications often require high-quality dialogue datasets, which are scarce for certain under-resourced mental health conditions like gambling disorders. Privacy concerns and the sensitive nature of mental health issues make real-world data collection difficult and time-consuming. We propose a framework that uses publicly available social media posts as seeds to generate empathy-driven multi-turn dialogues between help-seekers and supporters in mental health conditions that have been under-researched in NLP. Through a case study on gambling disorder, we demonstrate the effectiveness of our approach in producing higher-quality dialogues than standard role-playing prompts. Our work showcases an effective dataset generation method for fine-tuning mental health language models alongside downstream applications like dialogue system development, particularly for low-resourced mental health disorders like gambling disorders.
%U https://aclanthology.org/2026.findings-aacl.54/
%P 1055-1073
Markdown (Informal)
[Synthesizing Dialogues from Social Media Data for Mental Health Support: A Case Study on Gambling Disorders](https://aclanthology.org/2026.findings-aacl.54/) (Zhang et al., Findings 2026)
ACL