-->PONE-D-25-15257-->-->LLM-impersonated debate contributions are more authentic, relevant and coherent than their original: A representative study using BBC1’s Question Time-->-->PLOS ONE

Dear Dr. Herbold,

Thank you for submitting your manuscript to PLOS ONE   . I am pleased to inform you that, following peer review, the reviewers and I found your study to be original, clearly written, and addressing a timely and important topic. Both reviewers recognized the relevance and ambition of your work and appreciated its methodological clarity and potential contribution.. I am pleased to inform you that, following peer review, the reviewers and I found your study to be original, clearly written, and addressing a timely and important topic. Both reviewers recognized the relevance and ambition of your work and appreciated its methodological clarity and potential contribution.

==============================

However, they also identified several issues that need to be addressed before the manuscript can be considered for publication. Based on their feedback, I am inviting you to submit a major revision   . Below, I summarize the main points raised by the reviewers and provide some guidance on how to proceed.. Below, I summarize the main points raised by the reviewers and provide some guidance on how to proceed.

<h3 data-end="1014" data-start="953">1. Contextual and interpretive alignment (Reviewer 1)   </h3></h3>

Reviewer 1 commends the design and analysis but raises concerns about the contextual asymmetry between the original human responses and the LLM-generated ones. Since the human content was transcribed from a live television programme (“Question Time”), it may include disfluencies, references, or transcription artifacts that affect perceptions differently than the directly generated LLM text. The reviewer suggests adding a post-hoc robustness check or qualitative analysis to demonstrate that these contextual or transcription-related differences do not drive the main effects.

Relatedly, Reviewer 1 encourages you to expand the discussion of content differences between human and impersonated responses. Specifically, the paper would benefit from showing how    these differ substantively; for example, by analyzing whether impersonations diverge in stance or argumentation, rather than merely in surface-level content. This would clarify whether the framing of “misrepresentation” is warranted.these differ substantively; for example, by analyzing whether impersonations diverge in stance or argumentation, rather than merely in surface-level content. This would clarify whether the framing of “misrepresentation” is warranted.

Finally, the reviewer notes that the reliability of human ratings is modest and recommends greater transparency about inter-rater variability, or, if feasible, additional averaging or collection of ratings to reduce subjective noise.

Minor notes include adding a brief explanation that Question Time    is a television programme, clarifying “random speaker” in the methods, and checking for potential answer length differences that could influence results.is a television programme, clarifying “random speaker” in the methods, and checking for potential answer length differences that could influence results.

<h3 data-end="2561" data-start="2496">2. Conceptual and methodological refinements (Reviewer 2)   </h3></h3>

Reviewer 2 finds the study compelling but identifies several areas where methodological clarification is needed:

• Construct clarity:    The meaning of The meaning of authenticity    should be defined more precisely, whether it captures authorship likelihood, realism, or plausibility. Reviewers recommend clarifying how this construct differs from related ones (e.g., coherence or content similarity) and ensuring that participants understood it consistently.should be defined more precisely, whether it captures authorship likelihood, realism, or plausibility. Reviewers recommend clarifying how this construct differs from related ones (e.g., coherence or content similarity) and ensuring that participants understood it consistently.
• Experimental design:    Provide more detail on the randomization and balancing of stimuli across conditions. If possible, include or reference any inter-rater checks used during the manual screening of generated responses.Provide more detail on the randomization and balancing of stimuli across conditions. If possible, include or reference any inter-rater checks used during the manual screening of generated responses.
• Measurement approach:    Consider complementing subjective Likert judgments with an objective or behavioral indicator (e.g., response times or accuracy), or at least acknowledge the limitations of relying solely on self-reported data.Consider complementing subjective Likert judgments with an objective or behavioral indicator (e.g., response times or accuracy), or at least acknowledge the limitations of relying solely on self-reported data.
• Statistical analysis:    The reviewer suggests using non-parametric or mixed-effects models better suited to ordinal and nested data, reporting medians and interquartile ranges, and revising effect size measures accordingly. You might also re-evaluate inter-rater reliability using intraclass correlation coefficients.The reviewer suggests using non-parametric or mixed-effects models better suited to ordinal and nested data, reporting medians and interquartile ranges, and revising effect size measures accordingly. You might also re-evaluate inter-rater reliability using intraclass correlation coefficients.
• Interpretation:    Finally, temper the strength of the claims about deception or indistinguishability between AI and human responses. Reviewer 2 encourages reframing your findings as perceptual rather than deceptive effects, potentially driven by linguistic fluency or stylistic uniformity.Finally, temper the strength of the claims about deception or indistinguishability between AI and human responses. Reviewer 2 encourages reframing your findings as perceptual rather than deceptive effects, potentially driven by linguistic fluency or stylistic uniformity.

Both reviewers found your study promising and well written. Their feedback is intended to help you enhance the rigor, clarity, and interpretive precision of your work. I encourage you to take this as a positive step toward publication; since the paper already has a strong foundation and, with the recommended revisions, could make a meaningful contribution to ongoing debates on authenticity, impersonation, and LLM-mediated communication.

==============================

Please submit your revised manuscript by Dec 03 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions-->

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1. Is the manuscript technically sound, and do the data support the conclusions?

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Reviewer #1: Yes

Reviewer #2: Yes

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-->2. Has the statistical analysis been performed appropriately and rigorously?-->

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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-->5. Review Comments to the Author

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Reviewer #1:    I found this paper interesting to read and easy to follow. The experimental design and methods were well motivated and clearly explained. The statistical analysis is sound. The results were generally well presented.I found this paper interesting to read and easy to follow. The experimental design and methods were well motivated and clearly explained. The statistical analysis is sound. The results were generally well presented.

While my overall opinion on the paper leans positive, I have three main concerns, which I believe could at least be partly addressed in revisions:

1) QT is a television programme, where panellists give live answers to audience questions. This means that the real human responses that form the basis of this paper are transcribed from speech that occurred in a specific context. The LLM, on the other hand, generated text directly in a decontextualised setting, as provided by the system prompt (lines 84ff).

The authors acknowledge this discrepancy (e.g. line 443) but do not meaningfully engage with it. Fundamentally, human responses are not being evaluated in the setting and modality for which they were intended.

The paper would be stronger if it demonstrated that this discrepancy does not affect results. For example, human responses may include markers (such as references to other panellists names, disfluencies) that are perceived as inauthentic, incoherent, etc. when transcribed and decontextualised, but not when spoken on the programme. There may also be transcription errors, which LLMs would not have. This seems in scope for post-hoc analysis.

2) The paper finds that original content is different from impersonated content and concludes from this that AI may be used to generate targeted misinformation about the speaker’s point of view (lines 339ff). However, there is no analysis into *how* content differs in human vs LLM-impersonated content, and whether this difference does in fact correspond to “misrepresentation”. For this, it would be necessary to show that the LLM-impersonated answers differ from the human answers in their issue-specific stance.

It seems very plausible that LLMs follow different lines of argumentation but ultimately make similar points, which may (hypothetically) even be endorsed by the original speaker. Asking only about “difference in content”, which may be explained in many ways, cannot capture this. Consequently, the framing of the paper as relating to misinformation and deception seems only partly warranted.

3) The paper claims that human judgments are reliable but does not provide strong evidence for this. Agreement is modest at best (lines 359 following), and the supplementary variables show low agreement on several variables. Clearly, there is substantial noise in the data from subjective rating differences, which could be addressed by collecting and averaging across more ratings. If not, it would still be useful to be more open and specific about the (lack of) human agreement in the main body of the paper.

Minor notes:

- I found no mention of answer length in the paper. It seems very plausible that there is a difference in length between human and LLM answers, which may in turn affect at least some of the analyses. Some normalisation would likely be necessary?

- It may help the (non-UK) reader to note in the Data section that Question Time is a TV programme. The only mention of TV / television right now, I believe, is in the intro.

- On first reading, I was a bit confused by lines 117-119: “random speaker” may need brief explanation. Of course, this becomes clear later.

Reviewer #2:    The manuscript explores the capacity of large language models (LLMs) to impersonate public figures in political debates. The topic is timely and uses an interesting dataset. The paper is well written and methodologically ambitious. However, the strength of the conclusions is weakened by shortcomings in the experimental design and in the statistical analysis. The findings are interesting but should be interpreted with greater caution, given the limitations discussed below.The manuscript explores the capacity of large language models (LLMs) to impersonate public figures in political debates. The topic is timely and uses an interesting dataset. The paper is well written and methodologically ambitious. However, the strength of the conclusions is weakened by shortcomings in the experimental design and in the statistical analysis. The findings are interesting but should be interpreted with greater caution, given the limitations discussed below.

1. Experimental Design

The overall three-track design is conceptually sound allowing the author to assess perceptions under different conditions. Nonetheless, the construct of authenticity is insufficiently defined. It remains unclear whether participants interpreted it as authorship likelihood, realism, or plausibility, which may conflate distinct psychological dimensions. Moreover, each participant evaluated only a few items, which limits the reliability of individual responses. Besides, more details about the randomization of questions and speakers is required to ensure data is balanced. The manual inspection of generated responses lacks formal coding criteria or inter-rater checks, reducing transparency and reproducibility.

Suggestions for improvement:

Clarify the exact wording and intended meaning of each measure, particularly “authenticity.” Provide evidence that the randomization of stimuli was balanced across conditions. Include or reference inter-rater checks for the manual screening of generated responses.

2. Measures and Data Collection

The study relies entirely on self-reported Likert-scale judgments. This approach is valid for perceptual studies, but complementary behavioral or comprehension-based measures (e.g., response time, detection accuracy) would have strengthened construct validity. Some measures—especially “content similarity”—may overlap conceptually with “authenticity,” potentially introducing redundancy.

Suggestions for improvement:

Consider clarifying the independence of constructs and, if possible, provide an additional objective indicator of perceptual discrimination between real and impersonated responses. Provide information about possible moderator factors.

3. Statistical Analysis

The paper presents a detailed description of the statistical workflow and justifies the use of non-parametric tests. However, several inconsistencies remain. The analysis averages ordinal Likert data and reports means, standard deviations, and Cohen’s d, assuming interval properties. This is not strictly correct for ranked data and can inflate effect sizes. Inter-rater reliability (reported Cronbach’s α) indicates moderate agreement at best, suggesting considerable noise in the judgments. The treatment of each outcome variable (authenticity, coherence, relevance, content) as independent is also problematic, as they are conceptually and statistically correlated. Finally, while some corrections are applied, the overall analytical structure would benefit from mixed-effects or ordinal regression models that account for the nested and repeated nature of the data.

Suggestions for improvement:

Report medians and interquartile ranges alongside or instead of means. Use effect size measures suited for ordinal data (e.g., rank-biserial correlation or other relevant measure.) Reassess reliability using intraclass correlation or a mixed-model framework. Consider a multivariate or mixed-effects approach to handle correlated measures and participant/item dependencies.

4. Interpretation of Results

The finding that impersonated responses are perceived as more authentic, coherent, and relevant than the originals is statistically supported but likely overstated. Given the modest reliability and limited item sampling, the conclusion that participants “cannot discern” AI-generated content should be presented more cautiously. It is plausible that judgments reflect linguistic fluency rather than genuine attribution of authorship.

Suggestions for improvement:

Moderate the interpretive claims and emphasize the perceptual, rather than deceptive, nature of the results. Discuss alternative explanations such as stylistic consistency or linguistic polish in LLM outputs.

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Reviewer #1: No

Reviewer #2: No

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-->PONE-D-25-15257R1-->-->LLM-impersonated debate contributions are more authentic, relevant and coherent than their original: A representative study using BBC1’s Question Time-->-->PLOS One

Dear Dr. Herbold,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.-->--> -->-->

Please submit your revised manuscript by May 07 2026 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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Mohammad Salah Hassan, Ph.D

Academic Editor

PLOS One

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Additional Editor Comments :

Dear Authors,

The revised manuscript, PONE-D-25-15257R1, entitled “LLM-impersonated debate contributions are more authentic, relevant and coherent than their original: A representative study using BBC1’s Question Time,” has now been reviewed.

Please accept my sincere apologies for the delay in reaching this decision, as one of the invited reviewers did not reply and additional time was therefore needed to secure a further review.

Reviewer 1 recommends acceptance and confirms that the authors have adequately addressed the comments raised in the previous round. The reviewer considers the manuscript technically sound, the statistical analysis appropriate, the data availability satisfactory, and the language clear and intelligible.

A few very minor points remain, but these should be treated as final editorial clean-up rather than substantive revisions. In line with the reviewer’s observations, the authors should clarify the limitation concerning the mismatch in context and modality between the original human responses and the LLM-generated responses, and moderate the “threat” framing where the wording may still appear stronger than warranted. In addition, a few minor editorial inconsistencies should be corrected in the final files, including residual typographical and spacing issues, consistency of the manuscript title across all submitted documents, and the final Data Availability link/DOI.

These minor matters do not affect the overall positive recommendation. Based on the reviewer’s report and my own assessment, I recommend acceptance of the manuscript subject to final minor editorial polishing.

Kind regards,

Mohammed Salah Alazzawi, PhD

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Reviewer's Responses to Questions

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Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

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Reviewer #1: Yes

Reviewer #3: Yes

**********

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Reviewer #3: Yes

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-->6. Review Comments to the Author

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Reviewer #1: I thank the authors for their detailed response to my first review. My concerns have largely been addressed, and I am happy to recommend acceptance. However, I would encourage the authors to engage with the following points:

1) I still believe that the mismatch in context and modality between the original human responses and the LLM-generated responses cannot be ruled out as at least a partial explanation of results. Length and prevalence of spelling errors may not individually have strong correlations with perceptions of authenticity etc., but their combination (and other unobserved factors) may still partially explain human judgments. A true like-for-like comparison would compare human-written textual responses to LLM-written responses. Since this comparison cannot be made, I think this should be clearly acknowledged as a limitation in the main body discussion.

2) I would encourage the authors to be clearer in their “threat model”: What exactly is the new threat created by LLM impersonation? And to what extent do the experiments in this paper measure this new threat? For example, I would argue that not all “made-up content” is equally problematic – LLM impersonation poses a threat only if the LLM-generated content *misrepresents* the speaker’s views *while still being perceived as authentic*. This is not something the paper tests for! The small qualitative analysis shows that there are 13 of 50 cases where the LLM-generated response misaligns in stance, but this is not enough to make claims about general LLM tendency to misrepresent, or how this misrepresentation impacts authenticity judgments. Personally, I would therefore tone down the “threat” language throughout the paper and simply provide a sober description of results related to authenticity, and pointers to open questions for future work.

Minor notes:

- line 18: typo and missing whitespace

- line 184: missing whitespace

Reviewer #3: This paper has been significantly improved. Thank you very much for your efforts in making the manuscript stronger and more polished after the revision.

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Reviewer #3: No

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LLM-impersonated debate contributions are more authentic, relevant and coherent than their original: A representative study using BBC1’s Question Time

PONE-D-25-15257R2

Dear Dr. Authors,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Mohammad Salah Hassan, Ph.D

Academic Editor

PLOS One

Additional Editor Comments (optional):

Dear Authors,

Thank you for submitting the revised version of your manuscript, “LLM-impersonated debate contributions are more authentic, relevant and coherent than their original: A representative study using BBC1’s Question Time.”

The manuscript has improved substantially, and the concerns raised in the previous round have been adequately addressed.

I am pleased to inform you that I recommend acceptance of the manuscript for publication in PLOS ONE.

Kind regards,

Reviewers' comments: