Dear Dr. Zada,

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Muhammad Shahid Anwar

Academic Editor

PLOS ONE

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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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: I Don't Know

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: No

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

Reviewer #4: Yes

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Reviewer #1: How does the proposed semantic consistency validation layer differ in a fundamental way from existing multimodal fusion approaches, and what makes it unique?

Could the authors more clearly articulate which specific shortcomings of prior cyberbullying detection methods (e.g., sarcasm detection, multimodal misalignment) this work directly addresses?

While the literature review is comprehensive, it is largely descriptive. Can the authors provide a more critical comparative analysis of limitations in previous multimodal models to justify their framework?

What precise mechanism enforces semantic consistency across modalities in the validation layer? For example, are there specific loss functions, thresholds, or constraints applied?

How balanced are the CAVD and SocialVidMix datasets, and what steps were taken to ensure annotation reliability (e.g., inter-annotator agreement measures)?

The reported improvements in accuracy, precision, and recall are encouraging, but are these improvements statistically significant compared to the baselines?

The paper lists examples of misclassified samples, but how could these insights be used to further refine the fusion strategy or semantic validation in future work?

In the cross-platform experiments (TikTok, Instagram, YouTube Shorts), were the training datasets drawn from all three platforms, or was true cross-domain transfer tested?

Could the authors enhance the clarity of performance visualizations (e.g., PR and ROC curves) by adding clearer axis labels, scales, or annotated thresholds for easier interpretation?

What are the key limitations of the current study (e.g., computational cost, dataset scope, language bias), and how might they impact real-world deployment of the model?

Reviewer #2: The manuscript presents an innovative and timely framework for detecting cyberbullying in short-form video content using a hybrid CNN-LSTM-Transformer approach. The experiments are thorough, and the discussion is rich, making the contribution valuable for both academic research and practical deployment. While the study is well-executed, a few minor revisions would further improve the clarity, consistency, and overall presentation.

Comments:

i. The abstract is informative but slightly long; condense by removing methodological details and emphasize contributions and real-world implications.

ii. Figures 2 and 3 captions should explain trends (e.g., “showing stable convergence and minimal overfitting”) instead of restating labels.

iii. Figure numbering should be consistent (avoid “Figure:” vs. “Figure -”).

iv. Table 3 and Table 5 require uniform decimal places (e.g., two digits throughout).

v. Add dataset names (CAVD, SocialVidMix) directly into captions of relevant tables for clarity.

vi. Consider merging repetitive keywords in the abstract (e.g., “Short-Form Videos” and “Video Content Moderation”).

vii. Some sections (e.g., 4.6 Discussion) cite older works; add at least one 2024–2025 citation to keep references current.

viii. The confusion matrix figure (Figure 4) would benefit from larger font size for readability.

References mix different styles, standardize capitalization and ensure uniform use of italics for journal titles.

Minor typographical inconsistencies exist in headings (e.g., spacing before section numbers).

Reviewer #3: The manuscript makes a strong and timely contribution to the field of online safety by presenting a multi modal hybrid framework for cyberbullying detection. The integration of BiLSTM , CNN, and Transformer models, along with semantic consistency validation, is thoughtfully designed and well supported by experimental evaluation. The cross-platform validation adds significant weight to the study’s robustness. The paper discussed good research ideas, but some changes needed for improvements. I suggested some recommendations below to further strengthen technical clarity and presentation.

1. streamline slightly and include a stronger statement on practical deployment (e.g., “real-time integration into moderation pipelines.

2. Figure 4 (confusion matrix) could include percentage labels in each cell to complement raw counts, improving readability, and for Figure 7 (ablation results), consider adding error bars to show variability across runs.

3. Ensure uniform decimal precision (e.g., 2 decimal places across all metrics in Tables 3, 5, and 6).

4. Briefly clarify whether early stopping was applied consistently across modalities or only at fusion level. This will help readers replicate the results.

5. Alongside accuracy, precision, recall, and F1, consider reporting Matthews Correlation Coefficient (MCC) or Cohen’s Kappa for imbalanced scenarios. This would add technical rigor.

6. Since both the datasets i.e. CAVD, SocialVidMix are English-centric, add a note that the framework could be extended to low resource languages or culturally diverse datasets.

7. Standardize References style (some entries mix APA/IEEE conventions, e.g., Ref. [20], [21]). Ensure DOIs or page ranges where available.

8. Balance the strong results with a short acknowledgment of limitations (e.g., scalability to longer videos, computational resource demands).

9. Reference numbers 17, 23, and 31 should be in their proper place.

10. How you validated your study. Justify it.

11. 11 ad future research directions.

12. Follow proper PLOS reference format. Some references are out of the PLOS format.

Reviewer #4: The authors should add more technical details to the abstract. Currently, it does not describe any, but it will provide an overview to the readers.

The authors do not explicitly mention in the introduction the challenges with the existing approaches that motivated them to propose a novel approach.

The motivation of the paper seems too generic. In the paper, the authors do not explicitly explain the motivation behind the proposed approach. What is different about the proposed approach from the previous research approaches? It should be described in the introduction. That should be the primary driver for the proposed approach. Currently, I haven’t configured what the proposed approach tries to achieve.

The authors should elaborate on contributions in the introduction. Currently, the paper doesn’t show any contributions.

The related work should be grouped into various subsections, such as NL-based approaches, machine learning-based approaches, and rule-based approaches, among others.

A comparative study with existing approaches will elaborate further on the proposed approach's key points.

The paper requires a comprehensive revision, as it contains numerous grammatical errors.

The authors should provide a rationale for why these particular algorithms are selected in the proposed approach; a few references will justify their use.

It is not clear whether the confusion matrix developed represents which deep learning algorithm accuracy results?

Calculating accuracy from the confusion matrix does not match the accuracy reported in Table 3.

The proposed approach lacks the limitations of the proposed study. Clarifying the study's limitations enables readers to better understand under what conditions the results should be interpreted. A clear description of the study's limitations also demonstrates that the researcher has a comprehensive understanding of the research.

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what does this mean? ). 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.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Reviewer #4: Yes: Javed Ali khan

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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 figures@plos.org . Please note that Supporting Information files do not need this step.

<p>AI-POWERED DETECTION OF CYBERBULLYING IN SHORT-FORM VIDEO CONTENT: A HYBRID DEEP LEARNING FRAMEWORK

PONE-D-25-36601R1

Dear Dr. Zada,

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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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,

Muhammad Shahid Anwar

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

Reviewer #3: All comments have been addressed

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

Reviewer #1: Yes

Reviewer #2: (No Response)

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

The PLOS Data policy

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: (No Response)

Reviewer #2: all comments are addressed. no more comment

all comments are addressed. no more comment

all comments are addressed. no more comment

Reviewer #3: Thanks for addressing all of my comments. The paper has been revised extensively and meet the publication standards.

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what does this mean? ). 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 Privacy Policy

Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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