Dear Dr. Pang,

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.

For acceptance, please validate the LLM labels with a small human double-coding and report precision/recall/F1; show counts and proportions with odds ratios and 95% CIs; add simple robustness checks for outliers, retweets/duplicates, and bots; clarify dataset timing, filters, and category scheme; share data/code per PLOS policy; soften causal language; update figures to include proportions with accessible colors; fix reference formatting and typos.

Nice to have: tighten the research gap and early definitions, and expand the discussion to acknowledge alternatives and limits.

Please include a short point-by-point response.

Please submit your revised manuscript by Nov 27 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.. 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.

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols....

We look forward to receiving your revised manuscript.

Kind regards,

Pierluigi Vellucci

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. We note that you have indicated that there are restrictions to data sharing for this study. For studies involving human research participant data or other sensitive data, we encourage authors to share de-identified or anonymized data. However, when data cannot be publicly shared for ethical reasons, we allow authors to make their data sets available upon request. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions.

Before we proceed with your manuscript, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information, data are owned by a third-party organization, etc.) and who has imposed them (e.g., a Research Ethics Committee or Institutional Review Board, etc.). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

b) If there are no restrictions, please upload the minimal anonymized data set necessary to replicate your study findings to a stable, public repository and provide us with the relevant URLs, DOIs, or accession numbers. Please see http://www.bmj.com/content/340/bmj.c181.long for guidelines on how to de-identify and prepare clinical data for publication. For a list of recommended repositories, please see https://journals.plos.org/plosone/s/recommended-repositories. You also have the option of uploading the data as Supporting Information files, but we would recommend depositing data directly to a data repository if possible.

Please update your Data Availability statement in the submission form accordingly.

3. Please ensure that you refer to Figures 4 and 5 in your text as, if accepted, production will need this reference to link the reader to the figures.

4. We note you have included a table to which you do not refer in the text of your manuscript. Please ensure that you refer to Tables 1 and 4 in your text; if accepted, production will need this reference to link the reader to the Tables.

5. If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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

Reviewer #1: Partly

Reviewer #2: Partly

Reviewer #3: Partly

**********

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

Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

3. 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.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

Reviewer #2: Yes

Reviewer #3: Yes

**********

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

Reviewer #1: The paper studies the impact of X's (formerly Twitter) abrupt termination

of its COVID-19 misinformation policy to investigate its effects on anti-vaccine discourse.

It applies pre/post design over a defined period (Nov 16–30, 2022),

using stratified sampling, keyword filtering, and classification via GPT-4o.

The use of GPT-4o for tweet classification and thematic labeling should be better justified.

In section 3.2, it is simply stated that "To verify annotation quality, we randomly sampled 200

labeled tweets for manual review by the research team and found that the LLM performed very well."

This point must be jusified in a more rigorous (statistical) manner.

On top of that, the manuscript does not explicitly state the total number of tweets analyzed

after filtering and annotation; if the classes are balanced and so on...

The paper should be made more self-contained to make it easier to read, especially when dealing

with importants points, eg :

- page 3: "assigned the identified antivaccine tweets into eight thematic categories [8]"

- page 6: "we applied an additional keyword filtering step inspired by [42]"

- page 7: "into eight thematic categories using GPT-4o, following the framework in [8]"

According to section 3.1, the COVID19 dataset is taken from ref. [41] that was published

in 2021. But the dataset of your study goes over the period Nov 16–30, 2022.

How do you explain this mismatch ?

All the figures show raw data (Tweet count). It would be more informative to have it in

percentage. Indeed, if the number of anti-vax Tweets increases together with the number of

pro-vax, what really matters, is the relative proportion of the two.

There are many bugs in the references that should be corrected:

[4] and [28] : the name of the journal does not appear

[10], [13], [14] : question marks appearing in the references

This shows that the bibliographic research was botched. Furthermore, there are numerous

X's studies about the COVID-19 into Plos One, and no one is quoted.

The major flaw of the study, as explained in section 5.4, is the short time Windows, covering

only seven days before and after the policy termination. There is a dissonance with the conclusions

of section 5.3 ("This study provides important insights into content moderation policies on social media

platforms and their broader implications for public health communication"; "X’s misinformation policy may

have played a crucial role in mitigating the spread of anti-vaccine discourse"; "anti-vaccine narratives

gained greater visibility") that should be re-written to take into account the small period of the study.

In other word, the conclusion should be way more modest, in order to be in phase with the study.

Reviewer #2: From my perspective, the title and abstract are both fine, clear, relevant, and well-executed; great work by the authors The Introduction is well-written and clearly structured; however, I suggest it could be improved by explicitly stating the research gap, defining key constructs like stance consistency and amplification earlier, and briefly framing the ‘marketplace of ideas’ concept to strengthen its theoretical grounding.

In the Related Work section, the authors mention that few studies explore the removal of moderation policies; however, I believe this distinction could be made sharper by more explicitly contrasting it with the broader literature on policy implementation and clarifying how the current study fills that specific gap in relation to the stated research questions.

In the Methodology section, the authors briefly mention that 200 annotated tweets were manually reviewed to verify GPT-4o’s performance, but I suggest including more detail, such as accuracy rates, confidence thresholds, or inter-rater agreement metrics, to improve transparency and support the robustness of the annotation process. While the inclusion of retweets is justified for amplification analysis, I suggest briefly discussing how potential duplicate content or bot-driven activity was handled or could have influenced the findings. The logistic regression models are appropriate, but I suggest briefly noting whether any control variables were considered to account for confounding factors, or clarifying the rationale for using a single binary predictor.

In the Results section, while the authors report that quoted tweets did not show a significant change post-policy, I suggest including the actual odds ratio and confidence interval to allow readers to interpret the strength and direction of the effect, even if not statistically significant. To help contextualize the reported odds ratios, I suggest including raw tweet counts before and after the policy shift, particularly for anti-vaccine tweets in each tweet type (original, quote, retweet).

In the Discussion section, the authors thoughtfully interpret their results and connect them to broader debates in misinformation and platform governance. However, I suggest expanding the discussion by addressing potential confounding factors beyond the policy change, such as algorithm shifts or concurrent news events, and by adding practical recommendations for platform moderation or public health communication strategies. It may also strengthen the paper to acknowledge the limitations of causal inference in a natural experiment design and the potential biases introduced by LLM-based classification.

In the Conclusion section, the authors provide a concise summary of their study, but I suggest enhancing it by including a brief reflection on limitations and offering directions for future research, such as analyzing longer-term impacts, cross-platform effects, or applying alternative annotation methods. The References section appears appropriate and well-cited overall; however, I suggest double-checking for consistency in formatting, and recommend including a few more recent studies on content moderation rollback or LLM-based misinformation detection if available.

Reviewer #3: I thank the authors for this submission. Understanding the impact of the removal of moderation policies is important, and I appreciate the analysis of anti-vaccination themes. However, there are some critical issues with the paper that must be addressed. Fixing these could be extremely easy or may take some more effort, depending on certain factors. Here are my comments:

- The most important issue with the paper is that the GPT-4o models (stance detection, topic detection, stance consistency) have not been validated. Not only should you report performance metrics like F1, precision, recall, etc., the members of the research team should independently annotate tweets for stance/topic, and the annotations should have sufficient inter-annotator agreement.

- The causal claims made in the paper should be toned down. I think it's a bit strong to say this is a natural experiment, since there was so much going on at this time that it's easy to imagine some other factor explaining the rise in anti-vaccine content. Furthermore, it's not clear what aspects of the moderation policy would have contributed to the increase in anti-vaccine content (is it just the announcement of the policy change that led people to post more content, or is it how the policy was actually enforced? Or is it just that so many people were laid off, there were fewer people to remove harmful posts?). In actuality, you are measuring the effect of the announcement of the removal of a moderation policy rather than the enforcement of the policy itself. I wouldn't call this a "natural experiment," and I would remove all statements making causal claims. I would also mention as a limitation that you can't infer causality.

- I would say prior literature largely disagrees with the way "echo chambers" are described in this paper. You should consider Barbera (2020), which describes how people are more likely to be exposed to cross-cutting content online than offline, and reviews the literature on the role algorithms play in such interactions. Guess et al. (2018) provide another convincing review. You also imply in the discussion that cross-cutting interactions would decrease polarization, but Bail et al. (2018) find the opposite effect, and Tornberg (2022) provides a potential explanation for why this might be. Please review this literature before submitting your revision, and consider removing or significantly altering your discussion about echo chambers.

- I am also curious how much of the increase in anti-vaccine content you observe can be explained by the big spike you see on day one, and how much of the increase in retweets can be attributed to the same tweets being retweeted a bunch of times. Is it possible that there was just a really viral tweet on the day the policy was enacted that results in the statistically significant changes? If you remove the outliers, will you still see a statistically significant result?

- I would also rework the plots. Please use a colorblind-friendly palette (i.e., not red/green). I also find the stacked bar chart difficult to read, since the axis for certain groups doesn’t start at zero, and the same stances for pre/post might not start at the same number. I would instead do three subplots for each tweet type, and have side-by-side bars for each stance showing the pre/post change.

- There's a typo in tables 6/7 (pro-policy instead of post-policy)

References:

1. Barberá, Pablo. "Social media, echo chambers, and political polarization." Social media and democracy: The state of the field, prospects for reform (2020): 34-55.

2. Guess, Andrew, et al. "Avoiding the echo chamber about echo chambers." Knight Foundation 2.1 (2018): 1-25.

3. Bail, Christopher A., et al. "Exposure to opposing views on social media can increase political polarization." Proceedings of the National Academy of Sciences 115.37 (2018): 9216-9221.

4. Törnberg, Petter. "How digital media drive affective polarization through partisan sorting." Proceedings of the National Academy of Sciences 119.42 (2022): e2207159119.

**********

what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at . PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.. Please note that Supporting Information files do not need this step.

Dear Dr. Pang,

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.

Overall, the study is technically sound and clearly reported. The methods and sampling/filtering steps are described in a transparent manner; the statistical approach is well documented (odds ratios and confidence intervals, stratification by tweet type, and robustness checks excluding highly retweeted content); and the GPT-4o annotation strategy is supported by external benchmark validation and cross-annotator agreement analyses, with limitations and a non-causal interpretation appropriately acknowledged.

I am therefore prepared to proceed with a minor revision, contingent on two targeted adjustments.

First, while references to Elon Musk are limited and largely presented as contextual background (for example, the termination of the relevant policy under his leadership and his publicly stated “free speech absolutist” and “digital town square” positions, with citations), the language in the Policy Implications discussion becomes more interpretive in places. Specifically, statements such as “our study demonstrates that in the absence of moderation…” and claims involving recommendation algorithms and “echo chamber dynamics” go beyond what is directly measured in the analyses. As the study is tweet-level and does not examine algorithmic curation or network structure, I recommend softening this section to avoid over-claiming and to maintain a strictly neutral tone. Concretely, please consider replacing “demonstrates” with “suggests” or “is consistent with,” specifying that the discussion concerns the termination of the COVID-19 misinformation policy rather than an “absence of moderation” in general, and either removing or carefully qualifying “echo chamber” language.

Second, the manuscript explains that the underlying data consist of tweet IDs from the publicly available Banda et al. dataset and that tweet text and user information cannot be shared due to Twitter/X Terms of Service; it also notes that researchers can replicate the dataset by rehydrating tweet IDs from the original source. However, as currently written, the Data Availability Statement does not fully support practical reproducibility, because it does not explicitly provide (or clearly point to) the specific subset of tweet IDs used in the final analyses nor the derived labels (stance/theme/stance-consistency) linked to those IDs, and it indicates that prompts/code are available only upon reasonable request. Notably, in your response to journal requirements you state you will share the full list of tweet IDs used, the annotation outputs linked to tweet IDs, and all analysis code, enabling rehydration subject to X policies. Please align the published Data Availability Statement with this commitment, ideally via a permanent public repository for the tweet ID list, associated labels, and analysis code/prompts (even if tweet text cannot be redistributed).

With these two changes, I expect the manuscript to meet the journal’s publication criteria.

Best wishes,

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

Please submit your revised manuscript by Apr 25 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.. 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.

• A letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols....

We look forward to receiving your revised manuscript.

Kind regards,

Pierluigi Vellucci

Academic Editor

PLOS One

Journal Requirements:

If the reviewer comments include a recommendation to cite specific previously published works, please review and evaluate these publications to determine whether they are relevant and should be cited. There is no requirement to cite these works unless the editor has indicated otherwise.

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #3: All comments have been addressed

**********

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

Reviewer #1: (No Response)

Reviewer #3: Yes

**********

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

Reviewer #1: (No Response)

Reviewer #3: Yes

**********

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.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 #3: Yes

**********

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

Reviewer #1: (No Response)

Reviewer #3: Yes

**********

Reviewer #1: (No Response)

Reviewer #3: (No Response)

**********

what does this mean?). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our For information about this choice, including consent withdrawal, please see our Privacy Policy..-->

Reviewer #1: No

Reviewer #3: No

**********

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

To ensure your figures meet our technical requirements, please review our figure guidelines: https://journals.plos.org/plosone/s/figures

You may also use PLOS’s free figure tool, NAAS, to help you prepare publication quality figures: https://journals.plos.org/plosone/s/figures#loc-tools-for-figure-preparation.

NAAS will assess whether your figures meet our technical requirements by comparing each figure against our figure specifications.

Auditing the Impact of Social Media’s Policy Shift on Anti-Vaccine Discourse: A Large Language Model-Driven Empirical Study

PONE-D-25-17346R2

Dear Dr. Pang,

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.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact  and clicking the ‘Update My Information' link at the top of the page. For questions related to billing, please contact billing support....

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Pierluigi Vellucci

Academic Editor

PLOS One

Additional Editor Comments (optional):

Reviewers' comments: