PONE-D-25-21796How fake news can turn against its spreaderPLOS ONE

Dear Dr. Yuasa,

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Reviewers' comments:

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Comments to the Author

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

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

Reviewer #1: N/A

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: No

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript highlights a relevant problem regarding fake news management. A crucial point regarding opponent sources of disinformation is treated and, in principle, sounding. The manuscript is interesting and the underlying ideas are sufficiently well explained. However, without numerical experiments or at least a source of validation in a prescribed context makes the analysis of limited value, in my opinion. I strongly suggest to proceed in this direction before re-considering the manuscript for publication in PLOS One.

Reviewer #2: This paper presents a rigorous and conceptually compelling analysis of how disinformation can sometimes have a backfire effect—causing belief updates in the opposite direction to its intended aim. Using tools from communication theory and Bayesian inference, the authors show that when a rational receiver integrates two information sources—one genuine and the other tainted by disinformation—disinformation can be counterproductive if specific conditions are met. In particular, if the genuine source has a higher signal-to-noise ratio and the noise in the two sources is positively correlated, the combined effect may be to shift the receiver's belief away from the disinformation. The authors extend their model to continuous time, generalize it to multiple sources, and identify a second phenomenon—the "overshoot effect"—where excessive disinformation pushes preferences beyond the intended target, potentially toward more extreme alternatives.

1. Positioning in Relation to Prior Literature

The analysis here is distinctive and well-developed, but it exists within a growing literature showing that counterintuitive belief updates can result from rational inference. Two earlier contributions deserve explicit discussion:

Jern, A., Chang, K. M. K., & Kemp, C. (2014). Belief polarization is not always irrational. Psychological Review, 121(2), 206.

Cook, J., & Lewandowsky, S. (2016). Rational irrationality: Modeling climate change belief polarization using Bayesian networks. Topics in Cognitive Science, 8(1), 160–179. https://doi.org/10.1111/tops.12186

These papers demonstrate that belief polarization or divergence can arise in Bayesian agents when they differ in priors or latent worldviews. By contrast, the current paper holds priors fixed and instead focuses on source structure and noise correlation as drivers of reversal effects. This is a meaningful contribution: it isolates a novel channel through which rational agents can arrive at counterintuitive belief shifts—not because of differences in prior beliefs, but due to the structural interplay of multiple noisy inputs. Clarifying this relationship would help establish the paper’s place within this theoretical lineage.

2. Clarification of Model Assumptions

The reversal effect depends on a set of assumptions that, while clearly articulated in the manuscript, could be further examined in terms of their plausibility:

o That receivers are exposed to both high-quality and disinformation-containing messages;

o That they are unaware of the presence of disinformation;

o That the noise components in the two channels are positively correlated.

The authors acknowledge that these conditions may not always be met but could expand on real-world conditions where they are likely to hold—for example, shared narratives across partisan and mainstream news, or algorithmic content blending in social media feeds. A deeper discussion of these boundary conditions would help readers evaluate when and where the theory is most applicable.

3. Consideration of Human Cognitive Constraints

The model assumes a rational Bayesian receiver who integrates messages based on signal-plus-noise structure without bias or selective attention. This is analytically useful but overlooks the many well-established deviations from rationality in human information processing—such as motivated reasoning, source derogation, and confirmation bias. The authors should briefly acknowledge these factors and reflect on how they might interact with or obscure the modelled reversal effect. For instance, would confirmation bias neutralise the backfire effect? Could heuristic discounting of discordant signals block the reversal from emerging? Even a brief engagement with this literature would ground the analysis more firmly in interdisciplinary discussions.

4. Practical and Policy Implications

The findings have immediate relevance for debates on how to counter disinformation. Yet the policy implications are only lightly touched upon. The authors could expand on this by considering:

o Whether the model supports focusing on amplifying high-quality information rather than censoring low-quality content;

o Whether pre-bunking (forewarning) could interact with the reversal effect;

o How this mechanism might inform platform design or media literacy strategies.

The observation that the best counter to disinformation may be simply to improve the clarity and signal strength of truthful information is both powerful and actionable. Developing this thread would increase the paper’s relevance beyond theory.

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Reviewer #1: No

Reviewer #2: Yes: 

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How fake news can turn against its spreader

PONE-D-25-21796R1

Dear Dr. Yuasa,

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.

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Kind regards,

Carlos Carrasco-Farré

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The authors have addressed my comments. I think that the manuscript has reached a level sufficient for publication.

Reviewer #2: All comments addressed. No further concerns. Nothing further to say.

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