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REVIEW 3 major objections 5 minor 138 references

Understanding Fortunetelling with Large Language Models in China: User Practices, Perceptions, and Impacts on Beliefs and Decisions

T0 review · 3 major / 5 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read People turn to LLM fortunetelling for emotional support and self-reflection, not for accurate prediction, and it rarely changes the direction of their decisions.

desk verdict First systematic map of LLM fortunetelling in China; the qualitative core is solid, but the abstract's 'rarely change beliefs' claim is undercut by the paper's own pre/post numbers. read the letter →

arxiv 2607.15626 v2 pith:JNWATJMZ submitted 2026-07-17 cs.HC

Xueer Lin , Chenyu Li , Shuai Ma , Yuhan Lyu , Zhenhui Peng This is my paper
classification cs.HC
keywords LLMfortunetellingChinaemotionalsupportbeliefchangedecision-makingBarnumeffectconfirmationbiasqualitativestudy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

People in China are increasingly asking large language models to tell their fortunes, and this paper asks what that practice does to them. Analysing 1,045 social media posts and interviewing 20 users, it finds that people consult LLM fortune-tellers mainly for emotional comfort and self-reflection rather than for accurate prediction. The results rarely reverse a user's existing belief or decision; instead they nudge confidence levels and, occasionally, the timing of an action, such as delaying a resignation or postponing a competition. Users tend to accept the model's flattering, vague answers through confirmation bias and the Barnum effect, treating the exchange as a reassuring ritual. The paper matters because it positions AI fortunetelling as a normal everyday interaction that shapes how uncertainty is felt and expressed, even when it does not change the decision itself.

What carries the argument

The argument rests on a two-part qualitative apparatus. First, a manually coded taxonomy of 1,045 social media posts categorises foretold topics, emotions, credibility judgements, and perceived practicality. Second, a think-aloud interview study with 20 users includes a live trial in which each participant rates their confidence in a decision or predicted outcome before and after consulting the LLM; the pre/post scores supply the paper's quantitative handle on belief change. The interpretive machinery is the trio of motivated reasoning, confirmation bias, and the Barnum effect, which explains why vague, flattering outputs feel personally accurate and why they reinforce rather than redirect e

What would settle it

A longitudinal study with a representative sample tracking the same users' decisions over several months, or an experiment that randomly assigns users to positive, negative, and neutral forecasts and finds that the direction of their stated plans changes systematically with the forecast, would settle whether the claimed 'reinforcement, not redirection' effect is real or an artefact of a small, self-selected sample.

Watch

Extended reading notes

Core claim

The central discovery is that LLM fortunetelling functions less as a predictive tool and more as a vehicle for emotional reflection and meaning-making. Across the two studies, users approached the LLM with structured prompts rooted in traditional systems such as Bazi, asked about romance, career, academics, and even lost objects, and generally came away positive or amused. When predictions matched reality, users felt surprise and took it as validation; when outputs were vague, users projected their own situations onto them. In the interview trial, 15 of 20 participants shifted their pre/post confidence or decision-likelihood scores, by an average of about 9.5 points, yet the direction of the

Load-bearing premise

The claim that LLM fortunetelling 'rarely changes' beliefs and decisions rests on 20 volunteer interviewees, mostly young women recruited partly through the authors' own networks, plus a definition of 'change' that treats direction as the test while counting confidence shifts as mere calibration.

Editorial extensions

If this is right

  • LLM fortunetelling should be evaluated as an emotional-support or reflection tool, not by predictive accuracy, since that is how users themselves judge it.
  • Design interventions such as reflective prompts, entertainment-versus-support modes, and warnings about sensitive data may reduce over-reliance without destroying the comfort users seek.
  • Social media amplification of coincidental 'accurate' predictions can skew public perception of LLM reliability, even when individual users remain skeptical.
  • Over repeated use, vague and ingratiating outputs may weaken critical scrutiny, so the same practice that reassures users could also make them underestimate long-term risks.
  • Traditional Chinese divination frameworks such as Bazi structure how users prompt the LLM, meaning the model inherits the cultural authority of those systems.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A direct consequence the paper leaves implicit: if LLM fortunetelling mainly reinforces pre-existing inclinations, its practical risk may be less about bad advice and more about quietly closing off options the user had not already considered; a testable extension would measure whether the set of options users consider narrows after a session.
  • The claim that results 'rarely change decisions' rests on short-term, self-reported measures; an implication is that small confidence shifts, repeated over weeks, could compound into larger belief drift, which a longitudinal diary study could detect.
  • Because the mechanism is the Barnum effect, the same interaction pattern should appear with any fluent text generator, not just LLMs; a comparison condition using a scripted pseudo-random 'fortune' generator could isolate whether the LLM's persona matters at all.
  • The interview sample skewed female (15 of 20); if fortunetelling is a culturally gendered practice, the emotional-support finding may generalise mainly to women, and a mixed-gender replication could change the conclusions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. The paper investigates LLM-based fortunetelling in China through two qualitative studies: a descriptive content analysis of 1,045 Chinese social media posts from RedNote and Weibo, and semi-structured interviews with 20 users of LLM fortunetelling, including a think-aloud trial with pre/post confidence ratings. The social media analysis yields taxonomies of foretold topics, prompt structures, general attitudes, emotional reactions, perceived credibility, and perceived practicality. The interview study characterizes motivations (decision support, emotional comfort, entertainment, social substitution), perceptions (vague or ingratiating outputs, partial conviction), and impacts on beliefs and decisions (reinforcing existing inclinations, adjusting the timing of actions rather than reversing decision directions). The abstract's central claim is that users treat LLM fortunetelling more as emotional support than accurate prediction, and that results rarely change users' initial beliefs or decisions, while being associated with subtle mindset shifts and small behavioral adjustments.

Significance. If the central claim is properly supported, this is a timely contribution to HCI research on AI-mediated decision-making in self-referential, emotionally loaded everyday contexts, and to cultural studies of digital spirituality and platform-mediated uncertainty. The study has notable strengths: a two-study mixed qualitative design, inter-coder agreement reported (mean Cohen's kappa = 0.845, lowest 0.712), a transparent codebook with platform-stratified statistics, an interview protocol that includes pre/post confidence ratings rather than purely retrospective accounts, and candid acknowledgment of sample and generalizability limitations. However, the paper's central quantitative support for the abstract's 'rarely change' claim is not robust: the operationalization of 'change' is unspecified, the reported mean change is not reproducible from Table 4, and the same table shows nonzero pre/post shifts for 15 of 20 participants. These issues are local and addressable by reanalysis and reframing, but they are load-bearing for the main conclusion.

major comments (3)
  1. [Abstract; §3.2 Findings (RQ3); Table 4] The central claim that LLM fortunetelling 'rarely changes users' initial beliefs or decisions' is not operationalized, and the paper's own quantitative data point the other way. Table 4 shows nonzero absolute pre/post shifts for 15 of 20 participants (75%). Recomputing from Table 4 gives a mean absolute change of 9.35 points over all 20 participants (12.47 over the 15 nonzero shifters), not the reported 9.53 (SD = 8.84) for 'the remaining' participants. Also, the text says 17 participants rated confidence in the experiment, but Table 4 lists pre/post scores for all 20. If 'change' means any shift in confidence, 75% of the sample changed; if it means a direction reversal of a decision, a different analysis is needed. The authors should report direction-specific outcomes and define a threshold, or revise the abstract to describe 'modest confidence calibration' rather than 'rarely change.'
  2. [Phase 2 Procedure; Findings (RQ3)] The think-aloud trial lacks any no-LLM control or repeated baseline measurement, so the pre/post confidence shifts cannot be attributed to LLM fortunetelling specifically; regression to the mean, task engagement, or the act of verbalizing thoughts could produce similar changes. Given that RQ3 asks how users' beliefs and decisions are 'affected' by LLM fortunetelling, the causal language in the findings and abstract should be softened, or an appropriate control/comparison condition should be added.
  3. [Participants; Limitations] The interview sample is small (N = 20), heavily female (15/20), aged 20–43, and partly recruited through the authors' WeChat networks; only 7 of 67 invited RedNote creators participated. The limitations section acknowledges this, but the abstract's generalizing claim that LLM fortunetelling 'rarely changes' users' beliefs/decisions draws on this sample. At minimum, the claims should be qualified to the recruited sample, and recruitment response rates should be reported in the Participants section rather than only implied in the text.
minor comments (5)
  1. [Introduction] Typo: 'has boost a new accessible form' should be 'has boosted a new accessible form.'
  2. [Related Work / References] The citation 'Huizi, Yiwei et al. 2022' is incomplete and nonstandard; please provide the full author list and venue.
  3. [Table 4] The column header 'Confidence in Decision' is ambiguous because the text distinguishes 'Belief' confidence from 'Decision' likelihood. Rename to 'Pre' and 'Post' with explicit units or clarify the header.
  4. [Findings (RQ3)] The statement that '17 participants chose a topic to predict and rated their confidence' is inconsistent with Table 4, which shows pre/post ratings for all 20 participants. Please reconcile the count.
  5. [Discussion] The interpretation in terms of motivated reasoning, confirmation bias, and the Barnum effect is plausible, but these constructs were not directly coded or measured; the discussion should present them as an interpretive lens rather than as empirical findings of the study.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's findings are empirical and self-contained; the central claim is not derived from its own definitions or from self-citations.

full rationale

This is an empirical qualitative study, not a derivation chain. The social-media taxonomy is produced by open coding of 1,045 posts, and the interview findings are based on 20 self-reported interviews and a think-aloud trial. The abstract's claim that LLM fortunetelling 'rarely change[s] users' initial beliefs or decisions' is an interpretation of interview data, not a consequence of a definition or a fitted parameter. The cited prior work by the same research group (Ma et al. 2024a, 2024b) is used contextually to frame credibility dimensions and the notion of timing-vs-direction influence, but the substantive conclusions rest on the paper's own coded data and participant quotes; the citations are not load-bearing in the sense that removing them would collapse the argument. The coding scheme was informed by existing psychology and AI-assisted decision-making literature, and the discussion interprets the findings through established constructs such as confirmation bias and the Barnum effect, but the codes themselves are descriptive and the theoretical interpretation is applied post hoc rather than built into the definitions. The pre/post confidence ratings in Table 4 do show a discrepancy with the reported mean change (9.53 vs. recomputed 9.35 over all 20 participants, or 12.47 over the 15 who changed), and the term 'rarely change' is never operationalized relative to those scores; these are validity and reproducibility concerns, not circularity. No step in the paper reduces to its own inputs by construction, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no mathematical axioms or fitted parameters; its load-bearing inputs are empirical sampling and interpretive assumptions. The main strengths: two complementary data sources and reliability coding. The main burdens: the interviews are small and self-selected, the coding categories embed theoretical constructs that later reappear as conclusions, and the quantitative summary (mean confidence change 9.53) does not exactly match the table.

assumptions (5)
  • domain assumption Public RedNote/Weibo posts represent the population of Chinese LLM fortunetelling users.
    Data come only from two public platforms; private or non-sharing users are excluded (Limitations). The taxonomy's prevalence figures would shift under a broader sample.
  • domain assumption Self-reported belief/decision impacts in interviews correspond to real psychological or behavioral changes.
    RQ3 conclusions rely on what 20 participants say about mindset and timing shifts; no longitudinal tracking or objective behavioral data is collected.
  • domain assumption Established psychological constructs (motivated reasoning, confirmation bias, Barnum effect) are applicable interpretive lenses for this data.
    Discussion invokes Kunda 1990, Nickerson 1998, and the Barnum effect to explain the observed patterns; if these constructs don't transfer to LLM interaction contexts, the theoretical contribution weakens.
  • domain assumption Cohen's kappa of 0.845 indicates coding reliability sufficient to support the reported taxonomy.
    Mean inter-coder agreement is good but 'Humorous Commentary' was κ=0.712, and no adjudication details are given for disagreements after final coding.
  • domain assumption The 20-person interview sample (15F/5M, ages 20–43) captures the range of relevant user experiences.
    Small, gender-skewed, self-selected sample recruited partly via authors' networks; acknowledged as non-representative in Limitations.

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Cite this review

Pith. "Pith review of Understanding Fortunetelling with Large Language Models in China: User Practices, Perceptions, and Impacts on Beliefs and Decisions." pith.science (2026). https://pith.science/paper/JNWATJMZ

@misc{pith2026260715626,
  author       = {Pith},
  title        = {Pith review of: Understanding Fortunetelling with Large Language Models in China: User Practices, Perceptions, and Impacts on Beliefs and Decisions},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JNWATJMZ}},
  note         = {Machine review of arXiv:2607.15626}
}
read the original abstract

Fortunetelling is a cultural practice for navigating uncertainty, often associated with people's beliefs and decisions. Fortunetelling with recent large language models (LLMs) introduces new opportunities and risks. This paper conducts qualitative studies to understand users' practices, perceptions, and impacts of LLM fortunetelling in China. We first analyze 1,045 posts on Chinese social media, yielding a comprehensive taxonomy of the diverse foretold topics (e.g., career, romance), emotion reactions (e.g., surprise, worry), and perceived credibility (e.g., doubt, trust) of LLM fortunetelling. Then, we conduct interviews with 20 users of LLM fortunetelling. The findings indicate that users treat LLM fortunetelling as a tool less for accurate prediction but more for emotional support. While the fortunetelling results rarely change users' initial beliefs or decisions, they are associated with subtle mindset shifts, with some users reporting small behavioral adjustments. We discuss implications for gaining benefits from LLM fortunetelling.

Figures

Figures reproduced from arXiv: 2607.15626 by the authors.

Figure 1
Figure 1. Three topics of posts. Post content is translated from Chinese to English using the embedded translator of the browser. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Research flow of understanding fortunetelling with LLMs by social media data analysis and interview study. [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

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Pith tools

Reviewed August 1, 2026 · model on record in the stance chip above.