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| Natura: | Preprint |
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2026
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| Accesso online: | https://arxiv.org/abs/2605.16279 |
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| _version_ | 1866910224586113024 |
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| author | Zhang, Manyang Zheng, Jinyang Yan, Zhijun |
| author_facet | Zhang, Manyang Zheng, Jinyang Yan, Zhijun |
| contents | Online mental health communities (OMHCs) are tiered platforms that connect patients with licensed counselors through public Q&A forums and paid private consultations. Their two-tier structure creates a strategic dilemma for genAI integration. Conversational agents can provide scalable and timely responses to a broader set of patients, alleviating persistent supply shortages, but their large-scale presence may also reshape counselors' participation in providing nuanced expertise, emotionally sensitive support, and paid consultations, which are central to platform revenue and long-run sustainability. Leveraging a quasi-natural experiment from the integration of a genAI-based conversational agent in a leading OMHC, we examine how AI entry affects counselor participation. Using multiple identification strategies, we find that posting intensity increases significantly after AI integration, while average response length remains unchanged and per-post social recognition declines. Mechanism analyses show that AI improves responsiveness and expands patient engagement, enlarging counselors' opportunity sets, with activity partially reallocated from a nearby non-AI subforum. Counselors respond heterogeneously: intrinsically motivated counselors reduce participation, whereas economically motivated counselors intensify competitive effort. These dynamics generate cross-tier spillovers: inactive counselors experience declines in paid consultations, while those who increase public participation preserve or expand downstream demand. Overall, our findings show that in tiered professional platforms, demand expansion and competitive incentives can outweigh intrinsic crowding-out. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16279 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative AI and Two-Tiered Online Mental Health Communities Zhang, Manyang Zheng, Jinyang Yan, Zhijun Computers and Society Artificial Intelligence Online mental health communities (OMHCs) are tiered platforms that connect patients with licensed counselors through public Q&A forums and paid private consultations. Their two-tier structure creates a strategic dilemma for genAI integration. Conversational agents can provide scalable and timely responses to a broader set of patients, alleviating persistent supply shortages, but their large-scale presence may also reshape counselors' participation in providing nuanced expertise, emotionally sensitive support, and paid consultations, which are central to platform revenue and long-run sustainability. Leveraging a quasi-natural experiment from the integration of a genAI-based conversational agent in a leading OMHC, we examine how AI entry affects counselor participation. Using multiple identification strategies, we find that posting intensity increases significantly after AI integration, while average response length remains unchanged and per-post social recognition declines. Mechanism analyses show that AI improves responsiveness and expands patient engagement, enlarging counselors' opportunity sets, with activity partially reallocated from a nearby non-AI subforum. Counselors respond heterogeneously: intrinsically motivated counselors reduce participation, whereas economically motivated counselors intensify competitive effort. These dynamics generate cross-tier spillovers: inactive counselors experience declines in paid consultations, while those who increase public participation preserve or expand downstream demand. Overall, our findings show that in tiered professional platforms, demand expansion and competitive incentives can outweigh intrinsic crowding-out. |
| title | Generative AI and Two-Tiered Online Mental Health Communities |
| topic | Computers and Society Artificial Intelligence |
| url | https://arxiv.org/abs/2605.16279 |