Social world knowledge: Modeling and applications

Nir Lotan; Einat Minkov

doi:10.1371/journal.pone.0283700

Peer Review History

Original SubmissionJuly 5, 2022
20 Dec 2022 Decision Letter - Barbara Guidi, Editor PONE-D-22-18974Social World Knowledge: Modeling and ApplicationsPLOS ONE Dear Dr. Minkov, 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 Feb 03 2023 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. 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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. Please note that PLOS ONE has specific guidelines on code sharing for submissions in which author-generated code underpins the findings in the manuscript. In these cases, all author-generated code must be made available without restrictions upon publication of the work. Please review our guidelines at https://journals.plos.org/plosone/s/materials-and-software-sharing#loc-sharing-code and ensure that your code is shared in a way that follows best practice and facilitates reproducibility and reuse. 3. We note that you have stated that you will provide repository information for your data at acceptance. Should your manuscript be accepted for publication, we will hold it until you provide the relevant accession numbers or DOIs necessary to access your data. If you wish to make changes to your Data Availability statement, please describe these changes in your cover letter and we will update your Data Availability statement to reflect the information you provide. [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: No 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: The paper proposed an embedding method and used the co-follower network to learn social embeddings of entities. This method was used to perform two downstream tasks. The method suggested and use cases are interesting and provide implications for other researchers in this area. There are few items the author needs to address: - Figures appear blank in the submission version - Link to data/code is not provided. - Author could mention the selection bias regarding the model predicting personality features of the users. It could be that demographics are correlated with related information users provide on Twitter e.g., younger users provide more info on their bio thus MTurk work could do a better job in judging their demographics. - The paper is currently slightly too long particularly the abstract is wordy and hard to follow Reviewer #2: The paper proposes an interesting study and it proposes SocialVec. The topic of the paper is really interesting, however it is not clear how the evaluation part has been organized. More detailed should be included, in particular the section related to the comparison. It si completely unclear the choices concerning the comparison. Please clarify this point in order to understand if this section is important or not, as it is. ****** 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: Yes: Barbara Guidi ******** [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.0283700.r001
Revision 1
2 Feb 2023 Author Response We thank the Editors and the reviewers for their constructive comments. Below are our detailed answers to each of the comments. REVIEWER 1: There are few items the author needs to address: Q1.1: Figures appear blank in the submission version R: We believe to have followed the submission instructions, by which figures should be removed from the main file, and be submitted separately. Q1.2: Link to data/code is not provided. R: We declared that our data and code would be specified upon acceptance. Nevertheless, following this comment, we included these details in the revised manuscript. Specifically, the following text has been added to the Introduction section: “… We make the SocialVec framework and the resulting entity embeddings as learned and applied in this work accessible to the research community, and believe that this has the potential of making a significant contribution to exploring social world knowledge as reflected in Twitter. Our code is available at github.com/nirlotan/SocialVecTraining. Another respository that contains the entity embeddings, as well as an API for assessing entity similarity, is available at https://github.com/nirlotan/SocialVec.” Q: Author could mention the selection bias regarding the model predicting personality features of the users. It could be that demographics are correlated with related information users provide on Twitter e.g., younger users provide more info on their bio thus MTurk work could do a better job in judging their demographics. R: We agree that there could be a selection bias in labeling the socio-demographic characteristics of users by crowd workers. Unfortunately, there is no alternative labeling method that is bias-free. For example, asking users to actively provide their details would only attract a subset of the population; or, labeling users based on their self-descriptions on Twitter would be inherently biased to those users who tend to share personal information online. That being said, we kindly note that the dataset used in our experiments, which was labeled by MTurk crowd workers, was collected and annotated prior to our work by other researchers (Volkova et al.). It is a public dataset, for which there exist previously published results using alternative methods, which we compare our results against. Nevertheless, to clarify the point raised, we added the following text to Section `6.1 Dataset’: “We note that beyond the labels being subjective, the dataset may present a selection bias, e.g., due to varying levels of self-exposure on social media by different sub-populations. Yet, we evaluate multiple alternative methods using the same data, and in the same conditions, where this forms a viable evaluation setup.” In addition, we discuss potential general biases involved in automatically predicting the personal traits of users in Sections `7.2 Limitations’ and `7.3 Ethical considerations’. Q: The paper is currently slightly too long particularly the abstract is wordy and hard to follow. R: Thank you for this comment. We have made a substantial effort to make the whole paper less wordy and more fluent. Overall, this effort resulted in a reduction of the paper length by more than 10%. In particular, we have edited the abstract, having it shortened as a result by roughly 25% (from 368 to 283 words). A document that highlights the tracked changes is submitted as part of this revision, illustrating the many edits performed to the revised manuscript. REVIEWER 2: The paper proposes an interesting study and it proposes SocialVec. The topic of the paper is really interesting, however it is not clear how the evaluation part has been organized. More details should be included, in particular the section related to the comparison. It is completely unclear the choices concerning the comparison. Please clarify this point in order to understand if this section is important or not, as it is. R: Thank you for noting this issue. To clarify the setup of the evaluation, and the underlying motivations for this setup, we have created a new main section in the revised paper (Sec.4) which is titled `4. Evaluation’. We then added Section `4.1. Evaluation methodology’, which includes two main paragraphs. The first paragraph discusses `intrinsic vs. extrinsic evaluation’. Below are some excerpts from this paragraph: “Unfortunately, there are no relevant human-judged benchmarks that assess entity similarity. Instead, we take a direct look at inter-entity similarities by exploring the entities that are most similar to example entities of interest in the learned social embedding space. …. This intrinsic evaluation is presented and discussed in Section 4.2. Nevertheless, we mainly place our focus on extrinsic evaluation, where we gauge the utility of the learned social entity embeddings for end applications which involve social inference. First, we assess the political bias of news sources in terms of entity similarity in the social embedding space. Second, we predict the personal traits of individual Twitter users based on the social embeddings of entities that they follow. These studies are presented in Sections 5 and 6, respectively.” The second paragraph in Section 4.1 discusses our comparison against alternative methods and baselines. Here is an excerpt from this paragraph: “In evaluating SocialVec on end tasks, we review and compare our results against alternative methods, which have been designed and applied per those target tasks and experimental datasets. In addition, we contrast our social entity embeddings with existing entity embedding schemes, namely, Wikipedia2Vec and Wikidata RBG embeddings. … To the extent that SocialVec achieves higher performance on tasks of social inference, this implies that it is superior in capturing dimensions of social meaning.” We believe that this section improves the understanding of how we apply and evaluate the proposed framework. Attachments Attachment Submitted filename: Rebuttel_letter.pdf https://doi.org/10.1371/journal.pone.0283700.r002
14 Mar 2023 Decision Letter - Barbara Guidi, Editor Social World Knowledge: Modeling and Applications PONE-D-22-18974R1 Dear Dr. Minkov, 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, Barbara Guidi 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 ******** 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: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: 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. Reviewer #1: Yes Reviewer #2: Yes ****** 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: Yes ****** 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 author has address all of my comments and I'm happy with the current state of the manuscript. I do not have further comments. Reviewer #2: The quality of the manuscript has been improved and all the previous requests have been addressed. For this reason, the paper could be accepted as it is. ****** 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: Yes: Barbara Guidi ******** https://doi.org/10.1371/journal.pone.0283700.r003
Formally Accepted
20 Mar 2023 Acceptance Letter - Barbara Guidi, Editor PONE-D-22-18974R1 Social World Knowledge: Modeling and Applications Dear Dr. Minkov: 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. Barbara Guidi Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0283700.r004

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