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AI-mediated social support: the prospect of human–AI collaboration

Jingbo Meng et al.

Journal of Computer-Mediated Communication2025https://doi.org/10.1093/jcmc/zmaf013article
AJG 3
Weight
0.50

What the paper says

The rise of large language models (LLMs) expands opportunities for creating interpersonal messages. Building on artificial intelligent (AI)-mediated communication (AI-MC), this study examines how people use LLM-based chatbots to generate support messages and how patterns of human–AI collaboration shape message features and influence message evaluations of helpfulness and authenticity. We propose the process-adoption model, categorizing message generation into four patterns: human-only, AI-only, modified-AI, and AI-guided. Results showed that AI-only and modified-AI messages were more likely than human-only messages to include informational and emotional support, which in turn, enhanced viewers’ evaluations of message helpfulness and authenticity. AI-guided messages were more likely to provide reciprocal self-disclosure than AI-only messages, which enhanced perceived authenticity. Lastly, AI-guided messages were rated as more authentic than AI-only messages even after accounting for the mediating effects of message features. These findings provide a nuanced understanding of the AI-MC spectrum, and discussions are provided about human–AI collaboration in support provision.

6 citations

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https://doi.org/https://doi.org/10.1093/jcmc/zmaf013

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@article{jingbo2025,
  title        = {{AI-mediated social support: the prospect of human–AI collaboration}},
  author       = {Jingbo Meng et al.},
  journal      = {Journal of Computer-Mediated Communication},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1093/jcmc/zmaf013},
}

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AI-mediated social support: the prospect of human–AI collaboration

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.44 × 0.4 = 0.18
M · momentum0.65 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.