Multimodal detection of hateful memes by applying a vision-language pre-training model

Yuyang Chen; Feng Pan

doi:10.1371/journal.pone.0274300

Peer Review History

Original SubmissionApril 11, 2022
14 Jun 2022 Decision Letter - Liejun Wang, Editor PONE-D-22-10554Multimodal Detection of Hateful Memes by Applying a Vision-Language Pre-Training Model PLOS ONE Dear Dr. Pan, 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 Jul 28 2022 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. Please include the following items when submitting your revised manuscript: 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 https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Liejun Wang, Ph.D. 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 S1 Fig and S2 includes an image of a participant in the study. As per the PLOS ONE policy (http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research) on papers that include identifying, or potentially identifying, information, the individual(s) or parent(s)/guardian(s) must be informed of the terms of the PLOS open-access (CC-BY) license and provide specific permission for publication of these details under the terms of this license. Please download the Consent Form for Publication in a PLOS Journal (http://journals.plos.org/plosone/s/file?id=8ce6/plos-consent-form-english.pdf). The signed consent form should not be submitted with the manuscript, but should be securely filed in the individual's case notes. Please amend the methods section and ethics statement of the manuscript to explicitly state that the patient/participant has provided consent for publication: “The individual in this manuscript has given written informed consent (as outlined in PLOS consent form) to publish these case details”. If you are unable to obtain consent from the subject of the photograph, you will need to remove the figure and any other textual identifying information or case descriptions for this individual. [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? 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: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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. Reviewer #1: Yes Reviewer #2: Yes ****** 4. 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: Yes Reviewer #2: Yes ****** 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: This study has demonstrated that Vision-Language PTMs with the addition of anchor points can improve the detection of hateful memes that involve strongly correlated textual and visual information. However, I have the following issues: 1. The motivation of the paper is not clear in Section Introduction. What are the challenges involved? What solutions already exist for the problem you want to solve? What are their limitations and drawbacks? It lacks a critical analysis of these works and a positioning of the proposed solution concerning the state-of-the-art works. 2. It is not clear the contribution of this work for the advance of the state-of-the-art in this domain. 3. The experimental results may be discussed in detail fundamentally by providing critical analysis. 4. Add most recent references at appropriate places. Such as: https://link.springer.com/article/10.1007/s00521-021-06546-x https://link.springer.com/article/10.1007/s00521-022-07054-2 Reviewer #2: This paper presents a mode for hateful memes detection by integrating vision-language pre-trained models with random forest classifier. Experiments are conducted on Facebook Hateful Meme Dataset to evaluate the proposed model. Although the main idea is easy to follow, I have several concerns as following: - The main idea of this work seems a simple combination of two existing works (OSCAR and random forest classifier). - Several related works for vision-language pre-training are missing: [A] "Unified Vision-Language Pre-Training for Image Captioning and VQA." AAAI. 2020. [B] “Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network.” AAAI. 2021. - In Table 1, the performance in Test Acc. of OSCAR+RF is lower than Visual BERT COCO. - More ablated runs (e.g., Visual BERT COCO+RF) should be included for performance comparison. ****** 6. PLOS authors have the option to publish the peer review history of their article (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 ******** [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 figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0274300.r001
Revision 1
16 Jul 2022 Author Response Response to Reviewers Editors: 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 S1 Fig and S2 includes an image of a participant in the study. As per the PLOS ONE policy (http://journals.plos.org/plosone/s/submission-guidelines#loc-human-subjects-research) on papers that include identifying, or potentially identifying, information, the individual(s) or parent(s)/guardian(s) must be informed of the terms of the PLOS open-access (CC-BY) license and provide specific permission for publication of these details under the terms of this license. Please download the Consent Form for Publication in a PLOS Journal (http://journals.plos.org/plosone/s/file?id=8ce6/plos-consent-form-english.pdf). The signed consent form should not be submitted with the manuscript, but should be securely filed in the individual's case notes. Please amend the methods section and ethics statement of the manuscript to explicitly state that the patient/participant has provided consent for publication: “The individual in this manuscript has given written informed consent (as outlined in PLOS consent form) to publish these case details”. If you are unable to obtain consent from the subject of the photograph, you will need to remove the figure and any other textual identifying information or case descriptions for this individual. R: We appreciate your notifications on the manuscript style and informed consent of the cited figures. In the modification, we revised our manuscript following your comments and added the agreement of the figure publish. Reviewer #1: This study has demonstrated that Vision-Language PTMs with the addition of anchor points can improve the detection of hateful memes that involve strongly correlated textual and visual information. However, I have the following issues: 1. The motivation of the paper is not clear in Section Introduction. What are the challenges involved? What solutions already exist for the problem you want to solve? What are their limitations and drawbacks? It lacks a critical analysis of these works and a positioning of the proposed solution concerning the state-of-the-art works. 2. It is not clear the contribution of this work for the advance of the state-of-the-art in this domain. 3. The experimental results may be discussed in detail fundamentally by providing critical analysis. 4. Add most recent references at appropriate places. Such as: https://link.springer.com/article/10.1007/s00521-021-06546-x https://link.springer.com/article/10.1007/s00521-022-07054-2 R: We appreciate your efforts to point out these flaws in the manuscript, and we followed your suggestions to revise our draft. First, in the introduction, we explained the importance and dilemma of hateful meme detection nowadays, including limitations and drawbacks of different techniques. Besides, we highlighted our innovation of adding tags as anchor points which might enhance the capability of hateful meme detection. Second, the idea of the anchor point in VLP is one of the most critical progresses in the past two years. When we started this project, this idea had not been used in the downstream task of hateful meme detection. We believe our findings could inspire fellow researchers to adopt and improve this thought. At the same time, in the discussion section, we also pointed out some future research directions, such as adding more specific tags, including race, skin color, etc., which we believe can improve the efficacy of the detector. Third, in the last few weeks, we consecutively retrained our model and other eleven baselines four times on the Google Colab platform for a comprehensive comparison. The batch size was set to the largest value given the memory restriction. Then, to reduce the variance of estimating the performance of each model, all of them were trained and tested four times, and the averages of Acc. and AUROC were taken for comparison. We observed that our multimodal model with anchor points performed better than unimodal or other multimodal models without anchor points. These results support our idea mentioned above. At last, we appreciate your recommending those two related papers. In revision, we cited them to support our method part. Reviewer #2: This paper presents a mode for hateful memes detection by integrating vision-language pre-trained models with random forest classifier. Experiments are conducted on Facebook Hateful Meme Dataset to evaluate the proposed model. Although the main idea is easy to follow, I have several concerns as following: - The main idea of this work seems a simple combination of two existing works (OSCAR and random forest classifier). - Several related works for vision-language pre-training are missing: [A] "Unified Vision-Language Pre-Training for Image Captioning and VQA." AAAI. 2020. [B] "Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network." AAAI. 2021. - In Table 1, the performance in Test Acc. of OSCAR+RF is lower than Visual BERT COCO. - More ablated runs (e.g., Visual BERT COCO+RF) should be included for performance comparison. R: We'd like to express our sincere gratitude for your review and for providing these valuable comments and suggestions. We revised our manuscript thoroughly and added some essential information about our model. First, our model is based on anOSCAR+ backbone, which differs from other vision-language transformers by taking images, anchor point tags, and text caption triplets as inputs. Although our model can be considered as a combination of an optical character recognition (OCR) module, an object detection (OD) module, a fine-tuned OSCAR+ module, and a random forest (RF) classifier module, this idea and our results are inspiring. Still, there is a substantial space for future improvement on the automatic hateful meme detection using deep-learning techniques. The emergence of multimodal transformer shed light on achieving effective detection of hateful memes owing to its capability in cross-modal context semantic learning. From previous studies, multimodal transformers presented superior performances (Acc. about 60-65%) compared to conventional CNN models (Acc. about 50% - 51%) in this task. However, they are still very far away from human performances (Acc. about 85%). Under this milieu, we noticed that cross-modal correlation (so-called "ambiguity" issue) might limit the performance of multimodal transformers. As a solution, anchor points, one of the most critical ideas in multimodal deep learning in the past two years, is adopted in this study to connect image and text semantics. When we started our project, this model type had not been attempted in meme classification yet. We believe the results demonstrated by our study would provide justification for future research on such methods. In future research, we also plan to go further by employing other techniques, such as adding more specific tags, including race, skin tone, etc. These upgraded tags have the potential to improve model performance. In our revision, we explained this issue in the introduction and discussion. Second, we appreciate you for providing us with those two papers about unified VL transformers with different designs. In the revision, we discussed applying different VL transformers in hateful meme detection. Third, about the question that the Acc. of OSCAR+RF in the test set is lower than Visual BERT COCO, we admit that the data in Table 1 is not rigorous. In our previous version, we simply cited the results from the published literature instead of running these models on our own. In this revision, we retrained those baseline models and added another two multimodal models (Late fusion and ConcatBERT) on the same Goggle Colab platform with batch sizes set to the largest possible value given memory limitation. In addition, all were consecutively trained and tested four times and the mean values of their Acc. and AUROC were compared, which helped to reduce the variance of estimating model performance. As a result, we can see Transformer-based Vision-Language Pre-Training Models (VL-PTMs) with anchor point tags inputs performed better than conventional VL PTMs without those tags. However, after multiple training, we find the differences of Acc. and AUROC between OSCAR+FC and OSCAR+RF are very small (actually, in previous manuscript, this gap was also small). So, we speculated that the benefit of our model is majorly ascribed to the addition of the anchor point involvement (object tags) but not the last classifier. Following your comments, we also revised our viewpoint and discussion. Attachments Attachment Submitted filename: Response_to_Reviewers_2022.7.17.docx https://doi.org/10.1371/journal.pone.0274300.r002
26 Aug 2022 Decision Letter - Liejun Wang, Editor Multimodal Detection of Hateful Memes by Applying a Vision-Language Pre-Training Model PONE-D-22-10554R1 Dear Dr. Pan, 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 for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. 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, Liejun Wang, Ph.D. Academic Editor PLOS ONE Additional Editor Comments (optional): The reviewer has completed reviewing the manuscript and the authors have addressed the reviewer's concerns. Therefore, I recommend publishing the paper. 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 ******** 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: Yes Reviewer #2: (No Response) ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: (No Response) ****** 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 Reviewer #2: (No Response) ****** 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: Yes Reviewer #2: (No Response) ****** 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: Thank you for the response and revisions. You have successfully addressed the issues raised by the reviewers and the manuscript can be accepted for publication. Reviewer #2: (No Response) ****** 7. PLOS authors have the option to publish the peer review history of their article (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 ******** https://doi.org/10.1371/journal.pone.0274300.r003
Formally Accepted
2 Sep 2022 Acceptance Letter - Liejun Wang, Editor PONE-D-22-10554R1 Multimodal Detection of Hateful Memes by Applying a Vision-Language Pre-Training Model Dear Dr. Pan: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. 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. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Dr. Liejun Wang Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0274300.r004

Open letter on the publication of peer review reports

PLOS recognizes the benefits of transparency in the peer review process. Therefore, we enable the publication of all of the content of peer review and author responses alongside final, published articles. Reviewers remain anonymous, unless they choose to reveal their names.

We encourage other journals to join us in this initiative. We hope that our action inspires the community, including researchers, research funders, and research institutions, to recognize the benefits of published peer review reports for all parts of the research system.

Learn more at ASAPbio .