ePrints

Understanding AI-Mediated Emotional Support in Mobile Social Media Use: A Technology Probe Study

Lookup NU author(s): Yiwen Shang, Dr Yuzhang Ge, Dr Caroline ClaisseORCiD, Dr Christopher BullORCiD, Professor Dave KirkORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

Algorithmically curated social media feeds frequently trigger anxiety during everyday mobile use, yet little is known about how AI can provide momentary emotional support in these contexts. We present TriggerDetector, a large language model (LLM)–powered technology probe that provides AI-generated explanations of anxiety and coping suggestions during social media use. Through an in-the-wild deployment with RedNote users (n = 241), we examine how users engage with AI-mediated support in emotionally charged moments. We find that AI-generated explanations function as resonance-based sensemaking resources, helping users feel understood while often accepting multiple plausible interpretations. At the same time, AI outputs introduce a sense of authority that shapes both interpretation and action. While this authority can enable action, it may also lead users with low self-efficacy to interpret difficulty in following advice as personal failure. We derive design implications for mitigating implicit correctness and supporting user reflection and negotiation in everyday use.

Publication metadata

Author(s): Shang Y, Ge Y, Robinson LJ, Wang E, Claisse C, Bull CN, Kirk D

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: International Conference on Mobile Human-Computer Interaction (MobileHCI)

Year of Conference: 2026

Acceptance date: 04/06/2026

Date deposited: 16/06/2026

Publisher: ACM

ePrints DOI: 10.57711/zhpp-nf03

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