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arXiv · 2606.07231

Moodie: An Early-Stage Design Exploration for Supporting Fear of Missing Out with LLM-based Chatbots

Hsin-Yu Tsai·Jingxian Liao·Fu-Yin Cherng·Tzu-Hsiang Huang
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Abstract

The excessive use of social media has led to the challenge known as Fear of Missing Out (FoMO). Existing studies fail to provide accessible, interactive tools that focus on the emotional and cognitive aspects of FoMO. This work presents Moodie, a chatbot designed using Large Language Models to support emotion regulation and reduce FoMO. We conducted a formative study to understand the needs of individuals with FoMO and developed Moodie. Then, we conducted a preliminary evaluative study (N=21) to observe how participants interact with Moodie and a baseline chatbot (GPT-4o) over one week. The results show that while both Moodie and a baseline chatbot reduced FoMO to a similar extent, Moodie resulted in greater engagement and social connection. This finding raises interesting questions about the advantages of purpose-built chatbots compared to general-purpose models for mental health support. Future research will include chat log analysis, prototype refinements, and longitudinal evaluations.

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Hsin-Yu Tsai, Jingxian Liao, Fu-Yin Cherng, Tzu-Hsiang Huang. 2026-06-05. Moodie: An Early-Stage Design Exploration for Supporting Fear of Missing Out with LLM-based Chatbots. https://arxiv.org/abs/2606.07231

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