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arxiv: 2606.07231 · v1 · pith:PTHQAUW5new · submitted 2026-06-05 · 💻 cs.HC

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 This is my paper

Pith reviewed 2026-06-27 20:59 UTC · model grok-4.3

classification 💻 cs.HC
keywords Fear of Missing OutLLM chatbotemotion regulationmental health supportuser engagementsocial connectiondesign explorationpreliminary evaluation
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0 comments X

The pith

A purpose-built LLM chatbot reduces FoMO as effectively as a general model while increasing engagement and social connection.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper investigates whether an LLM chatbot specifically designed for Fear of Missing Out can address the emotional and cognitive sides of the problem better than existing tools. After a formative study to identify user needs, the authors created Moodie and ran a one-week comparison with 21 participants against a baseline general-purpose chatbot. Both approaches lowered FoMO levels by similar amounts, but Moodie produced noticeably more user interactions and stronger feelings of social connection. A sympathetic reader would care because this points to a practical way to deliver targeted emotional support through accessible chat interfaces without relying solely on broad AI models.

Core claim

Moodie is an LLM-based chatbot built to support emotion regulation for individuals experiencing FoMO; in a preliminary evaluative study it reduced FoMO to the same degree as GPT-4o yet produced greater engagement and a greater sense of social connection over one week.

What carries the argument

Moodie, the purpose-built LLM chatbot whose conversation design draws directly from a formative study of FoMO experiences to guide emotion regulation.

If this is right

  • Purpose-built chatbots can sustain higher engagement than general models for the same mental-health task.
  • LLM tools tailored to a specific emotional challenge can deliver measurable social-connection benefits alongside symptom relief.
  • Short-term deployments of such chatbots are feasible for initial testing of engagement effects.
  • Design choices informed by a formative study of user needs can differentiate outcomes even when symptom reduction is comparable.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the engagement advantage holds in larger samples, specialized chatbots could lower dropout rates in digital mental-health tools.
  • The social-connection finding suggests these tools might influence real-world social-media habits beyond the chat session itself.
  • Extending the approach to other emotion-regulation targets could test whether purpose-built design consistently outperforms general models.

Load-bearing premise

The one-week duration and sample of 21 participants are enough to reveal reliable differences in engagement and social connection between the two chatbots.

What would settle it

A follow-up study with more participants over multiple weeks that finds no reliable difference in daily interaction counts or social-connection ratings between Moodie and the baseline chatbot.

Figures

Figures reproduced from arXiv: 2606.07231 by Fu-Yin Cherng, Hsin-Yu Tsai, Jingxian Liao, Tzu-Hsiang Huang.

Figure 1
Figure 1. Figure 1: This figure shows examples of conversation for emotional support (left), practical suggestions (middle), and the [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces Moodie, an LLM-based chatbot for emotion regulation and FoMO reduction, developed via a formative needs study. It reports a preliminary evaluative study (N=21) comparing Moodie to a GPT-4o baseline over one week, finding equivalent FoMO reduction but higher engagement and social connection with Moodie. The work positions itself as an early-stage design exploration raising questions about purpose-built vs. general-purpose chatbots for mental health support.

Significance. If the comparative findings are substantiated with adequate statistical detail, the paper offers a concrete design case in HCI for tailoring LLMs to specific emotional challenges like FoMO. It contributes an initial prototype and observational data on engagement differences, which could inform future longitudinal work on chatbot personalization for mental health.

major comments (2)
  1. [evaluative study] Evaluative study section: The central claim of greater engagement and social connection for Moodie rests on an N=21 one-week comparison, yet the manuscript provides no description of the exact measures used for FoMO/engagement, the statistical tests performed, effect sizes, power analysis, or handling of dropouts/multiple comparisons. This gap prevents assessment of whether the reported advantage is reliable or vulnerable to noise.
  2. [abstract and evaluative study] Abstract and study description: The one-week duration and small sample are presented without justification or discussion of limitations for detecting between-condition differences, directly undermining the comparative claim that Moodie shows advantages over the baseline.
minor comments (1)
  1. [abstract] The abstract states results but does not preview the specific quantitative or qualitative metrics that support the engagement finding.

Simulated Author's Rebuttal

2 responses · 0 unresolved

Thank you for your constructive feedback on our early-stage design exploration. We address the major comments point-by-point below and will revise the manuscript to improve transparency on study methods and limitations.

read point-by-point responses

  1. Referee: [evaluative study] Evaluative study section: The central claim of greater engagement and social connection for Moodie rests on an N=21 one-week comparison, yet the manuscript provides no description of the exact measures used for FoMO/engagement, the statistical tests performed, effect sizes, power analysis, or handling of dropouts/multiple comparisons. This gap prevents assessment of whether the reported advantage is reliable or vulnerable to noise.

    Authors: We agree that the manuscript should provide more methodological detail for transparency. As this is framed as a preliminary observational study rather than confirmatory research, no a priori power analysis was performed and multiple-comparison corrections were not applied. In revision we will add: (1) exact measures (FoMO scale and engagement metrics such as message volume and session length), (2) the statistical tests used and any effect sizes computed, and (3) explicit statements on the absence of power analysis and dropout handling procedures. These additions will clarify the exploratory scope without overstating the findings. revision: yes

  2. Referee: [abstract and evaluative study] Abstract and study description: The one-week duration and small sample are presented without justification or discussion of limitations for detecting between-condition differences, directly undermining the comparative claim that Moodie shows advantages over the baseline.

    Authors: We accept that the abstract and study section require explicit justification and limitations discussion. The one-week timeframe was chosen to observe initial real-world engagement patterns, and N=21 reflects practical constraints of a week-long daily-interaction study. In the revised version we will add a short justification in the study description, update the abstract to note the preliminary nature, and include a limitations paragraph stating that the sample size and duration preclude strong inferences about between-condition differences, positioning the results as observational insights to guide future work. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical design study with independent measurements

full rationale

The paper is a qualitative/empirical HCI design exploration. It describes a formative study to gather user needs, followed by an evaluative study (N=21, one week) comparing Moodie to GPT-4o on FoMO reduction, engagement, and social connection. No equations, fitted parameters, predictions derived from inputs, or self-citation chains appear in the provided text or abstract. Reported outcomes are direct measurements from participant interactions and self-reports, not reductions of any claimed derivation to the study's own inputs or prior author work. This is self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is a design exploration and small-scale user study paper; it contains no mathematical derivations, fitted parameters, or postulated physical entities.

axioms (1)
  • domain assumption Self-reported measures collected over one week can provide preliminary signals about chatbot engagement and FoMO reduction
    Standard assumption in early-stage HCI formative and evaluative studies

pith-pipeline@v0.9.1-grok · 5707 in / 1223 out tokens · 36020 ms · 2026-06-27T20:59:48.983865+00:00 · methodology

discussion (0)

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Reference graph

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