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arxiv: 2606.19286 · v1 · pith:WLBDCRABnew · submitted 2026-06-17 · 💻 cs.HC · cs.AI· cs.CY

Correct Yourself, Keep My Trust: How Self-Correction and Social Connection Shape Credibility in Social Chatbots

Biswadeep Sen , Yi-Chieh Lee This is my paper

Pith reviewed 2026-06-26 19:12 UTC · model grok-4.3

classification 💻 cs.HC cs.AIcs.CY
keywords self-correctionsocial chatbotscredibilitytrustworthinesserror recoverysocial connectionbelief change
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The pith

Self-correction by social chatbots preserves trustworthiness and expertise ratings after errors, while social connection drives belief change only in self-correction cases.

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

The paper examines how social chatbots recover from mistakes and whether the method affects ongoing user trust. A between-subjects study with 120 participants compared three approaches: a webpage retraction, self-correction by the chatbot, and correction by an expert chatbot. All methods corrected the factual error equally, yet only self-correction avoided drops in ratings of trustworthiness and perceived expertise. Social connection, measured by social attraction and self-disclosure, predicted how much users updated their beliefs, but this link existed solely when the original chatbot handled the correction itself. The findings point to self-correction as a way for chatbots to maintain credibility while addressing inaccuracies.

Core claim

Self-correction by the social chatbot corrects errors as effectively as external sources but without reducing credibility ratings, whereas external corrections lower trustworthiness and perceived expertise. Social connection strength predicts the magnitude of belief change only under self-correction; outsourcing the correction removes this relationship entirely.

What carries the argument

Between-subjects experiment with three error-correction conditions (webpage retraction, self-correction by the social chatbot, expert-chatbot correction) and social connection (social attraction plus self-disclosure) as a moderator of belief change.

If this is right

  • Social chatbots should implement self-correction mechanisms to correct errors while retaining user trust.
  • Building social attraction and encouraging self-disclosure amplifies the effectiveness of corrections on belief updating.
  • Relying on external sources for corrections eliminates the benefit that social connection provides for belief change.

Where Pith is reading between the lines

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

  • The pattern may apply to other persistent conversational agents where repeated interactions matter more than one-off accuracy fixes.
  • Designers could test whether repeated self-corrections over multiple sessions compound into higher long-term retention than external corrections.
  • The moderator effect suggests relational factors interact with error-handling strategies in ways that purely informational fixes do not capture.

Load-bearing premise

The three correction conditions presented equivalent chatbot behavior, error content, and user exposure so that differences can be attributed to the correction strategy and its interaction with social connection rather than unmeasured factors like prior attitudes or consistency perceptions.

What would settle it

A follow-up study that equalizes perceived chatbot consistency across conditions and still finds no trustworthiness advantage for self-correction, or that finds social connection predicts belief change even under external correction, would undermine the central results.

Figures

Figures reproduced from arXiv: 2606.19286 by Biswadeep Sen, Yi-Chieh Lee.

Figure 1
Figure 1. Figure 1: Study design across the three correction conditions. All participants first completed the same three phases with Drew: [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Self-correction boosts credibility. Chatbots that cor [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Social connection amplifies belief change only un [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
read the original abstract

When social chatbots make mistakes, and they do, how they recover determines whether users trust them again. Social chatbots are increasingly integrated into everyday life, yet they remain prone to generating convincing but inaccurate information. The social connection they build with users makes such errors particularly consequential. We conducted a between-subjects experiment (N=120) comparing three error correction strategies: a webpage retraction, self-correction by the same social chatbot, and correction by an expert chatbot. Our results reveal two key findings. First, all three strategies corrected the error equally well, but only self-correction did so without damaging the chatbot's credibility: participants rated self-correcting chatbots significantly higher in both trustworthiness and perceived expertise than chatbots whose errors were corrected by external sources. Second, the strength of the user's social connection with the chatbot, measured through social attraction and self-disclosure, significantly predicted the magnitude of belief change, but only when the chatbot corrected itself. Outsourcing corrections to an external source severed this link entirely. These findings suggest that social chatbots should correct their own mistakes rather than outsource corrections, and that investing in social connection is a functional mechanism that amplifies correction effectiveness, not merely a design feature. We discuss implications for designing chatbots that maintain long-term credibility while effectively addressing their own errors.

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 manuscript reports a between-subjects experiment (N=120) comparing three error-correction strategies for social chatbots (webpage retraction, self-correction by the same chatbot, and correction by an expert chatbot). It claims that all three strategies correct factual errors equally well, but only self-correction preserves the chatbot's trustworthiness and perceived expertise; additionally, measures of social connection (social attraction and self-disclosure) predict the magnitude of belief change only in the self-correction condition.

Significance. If the results hold after addressing methodological gaps, the work offers a clear, actionable design recommendation for social chatbots and contributes empirical evidence on how social connection functions as a moderator rather than a mere feature. The between-subjects design and focus on belief change provide a falsifiable test of the self-correction hypothesis.

major comments (2)
  1. [Methods] The central claim that differences in trustworthiness, expertise, and the social-connection moderator are attributable to correction source requires that the three between-subjects conditions were equivalent in initial erroneous response wording and timing, subsequent interaction length and style, and absence of differential persona or consistency cues. The manuscript provides no scripts, logs, or manipulation-check data confirming this equivalence (Methods section).
  2. [Abstract] The abstract states directional results (self-correction rated significantly higher; social connection predicts belief change only for self-correction) but supplies no statistical details, effect sizes, participant demographics, or analysis methods. These details are required to evaluate whether the reported differences are load-bearing for the claims.
minor comments (1)
  1. [Abstract] The abstract could be expanded to include a brief statement of the statistical approach and key effect sizes to allow readers to assess the strength of the reported differences without immediately consulting the full results section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their thoughtful review and constructive suggestions. We address each major comment below and outline the revisions we will make to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Methods] The central claim that differences in trustworthiness, expertise, and the social-connection moderator are attributable to correction source requires that the three between-subjects conditions were equivalent in initial erroneous response wording and timing, subsequent interaction length and style, and absence of differential persona or consistency cues. The manuscript provides no scripts, logs, or manipulation-check data confirming this equivalence (Methods section).

    Authors: We agree that ensuring and demonstrating equivalence across the three conditions is essential to support our claims. The manuscript's Methods section described the procedure at a high level but did not include the full scripts or manipulation check data, which were collected during the study. In the revision, we will add the exact wording of the initial erroneous responses, the correction messages for each condition, details on interaction timing and length, and results from manipulation checks confirming no differential perceptions of persona or consistency. These will be incorporated into the main text or provided as supplementary materials. revision: yes

  2. Referee: [Abstract] The abstract states directional results (self-correction rated significantly higher; social connection predicts belief change only for self-correction) but supplies no statistical details, effect sizes, participant demographics, or analysis methods. These details are required to evaluate whether the reported differences are load-bearing for the claims.

    Authors: We acknowledge that the current abstract lacks the statistical details necessary for full evaluation. We will revise the abstract to include key information such as the statistical tests used, significance levels, effect sizes where appropriate, participant demographics (e.g., age, gender distribution), and a brief description of the analysis methods, while adhering to the word limit. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical experiment with no derivation chain

full rationale

The paper reports results from a between-subjects experiment (N=120) comparing error-correction strategies via participant ratings and regression on social-connection measures. No equations, first-principles derivations, parameter fitting, or predictions are present. Claims rest on statistical outcomes from collected data rather than any self-referential reduction, self-citation load-bearing premise, or ansatz. The study is self-contained as an empirical report; external benchmarks (participant responses) are independent of the analysis itself.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claims rest on the validity of self-report scales for trust and social connection plus the assumption that experimental conditions were equivalent; no free parameters or invented entities are introduced.

axioms (2)
  • domain assumption Self-report measures of trustworthiness, expertise, social attraction, and self-disclosure validly capture the intended psychological constructs.
    Standard reliance on established psychological scales in HCI studies.
  • domain assumption The between-subjects assignment isolates the effects of correction strategy without systematic differences in participant characteristics or chatbot presentation across conditions.
    Core assumption of the experimental design described in the abstract.

pith-pipeline@v0.9.1-grok · 5774 in / 1316 out tokens · 29750 ms · 2026-06-26T19:12:55.529925+00:00 · methodology

discussion (0)

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Reference graph

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