PONE-D-19-19498

Topic-driven Toxicity: Exploring the Relationship between Online Toxicity and News Topics

PLOS ONE

Dear Dr. Salminen,

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Based on the review comments from two experts in this field, all reviewers agree there are merits in this submission and the contributions are significant. At the same time, the reviewers also raised some concerns that are necessary to be addressed for publication. Based on my own reading, I fully agree with the review comments and hope the authors will address the concerns and revise the submission accordingly.

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Academic Editor

PLOS ONE

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Additional Editor Comments (if provided):

Based on the review comments from two experts in this field, all reviewers agree there are merits in this submission and the contributions are significant. At the same time, the reviewers also raised some concerns that are necessary to be addressed for publication. Based on my own reading, I fully agree with the review comments and hope the authors will address the concerns and revise the submission accordingly.

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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?

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

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

Reviewer #1: Yes

Reviewer #2: Yes

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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: No

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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

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5. Review Comments to the Author

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

Reviewer #1: This paper explores the relationship between the topics of news videos posted on Youtube (from the Al Jazeera Media Network), and the toxicity of viewers’ comments. The subject is interesting and timely. The methodology is also interesting, and generally well described. Results show that comment toxicity varies according to the topic presented, which leads the authors to suggest that news comment toxicity is topic-driven (targeting topics) rather than vindictive (targeting people).

While overall I think this is very interesting work, I have some concerns regarding the takeaways (or at least their current presentation) that I believe call for a revision. The paper would also benefit from another pass on typos and grammatical errors.

MAJOR CONCERN

On pages 25, 27, and 28, the authors should absolutely (!!!) remove the names of the persons they quote. Even if they are pseudonyms (which they do not seem to be), the authors should never include any clue that might allow to (re)identify individuals. This is an important breach of research ethics!

REGARDING TAKEAWAYS

In 6.1, the second recommendation seems dangerous to me. Although it follows a trend reported in other work, segmenting news audiences based on their worldview and curating content to comfort those views is precisely how thought echo-chambers are created. I strongly encourage the authors to nuance this point, and to offer some critical reflection on it. For example, while it might be a desirable economic outcome for the outlet, it is very likely not a desirable social outcome.

I am not sure what the very last point in 6.2 adds to the discussion. The authors seem to imply that journalists should not use sources, as they might fuel hostility in comments. This is absurd. More generally, I am a bit concerned by the tension that exists throughout Section 6.2 between adapting journalistic practices to avoid online toxicity in comments, and, as the authors put it: “obviously, [avoiding sensitive topics] is not a good strategy.” While the authors do try to make it explicit that news outlets should not submit to the “tyranny of the audience”, their main arguments and suggestions seem to do exactly that, e.g., by depoliticizing content, or by applying a “humanistic” formula on every story. I recommend the authors rethink and rewrite this section to reduce this tension—especially in light of the first paragraph of 6.3.

OTHER COMMENTS

I think the beginning of Section 3 could be made clearer. What I understand from 3.3 is that the Neural Net is trained on (textual) articles from Al Jazeera’s website, which are tagged with topic labels, and then used to predict the topics of Youtube videos, using their titles and descriptions. Is this correct? This was not clear to me in 3.1.

Further, are all the articles scraped form Al Jazeera’s website in English? The authors seem to imply their data collection is limited to English in 3.4, but do not mention it explicitly.

Is each article on Al Jazeera’s website tagged only once, or can the same article have multiple tags? i.e., is the video-topic-prediction problem a multi-class or multi-label classification problem? The latter would then also complexify the study of the relationship between topic and comment toxicity. That said, it seems from the end of 3.6 that the authors’ Neural Net only outputs one label per video.

I appreciate the authors’ attempts to bring external validation to each step of the data collection and (automatic) annotation process.

I wonder how well connected the comments for a given video are to the topic of that video. Do viewers always stick to what they have watched in the video? Or might they write about other, unrelated topics? Admittedly, the authors do mention this in their discussion, but it would be good to state it earlier in the paper. Also, does each video contain only one topic? (this is related to the multi-label problem mentioned above). Assessing this seems important, since topic toxicity is simply calculated by subsequently averaging the comment toxicity for each video, and the video toxicity for each topic (see 3.8).

I am not sure I understand the procedure for the aggregation of topics into superclasses. Did only one author do this? (Which seems to be the case, but the sentence mentioning this is not clear) Or did several? Also, is there overlap between News Topics and Countries and Regions, or are the topics that are combined into each of these superclasses distinct?

Section 4.2 is very unclear to me. I suggest it be entirely rewritten, possibly condensed, and moved after 4.3 (at least after table 5). What is beta in the equation and in the null hypothesis?

Can table 6 be fit into a single page? Also, the description in Section 4.3 mentions color in the matrix, but I do not see it. It would be good to highlight significant differences in the pairwise comparisons.

In Section 5, the authors mention reading through and (manually?) coding the comments and discussions under the videos. How was the coding done? How many coders were there?

In the subsection on Platform’s power, the authors do not prove there is a causal relationship between Google/Youtube’s description of the news outlet and the comments the they highlight. I understand the commenters are making statements based on the relationship between Al Jazeera and the Qatari government, but claiming this is directly linked to the phrasing of the Youtube tag is a bit of a stretch—which, again, is not empirically proven. What is the proportion of comments that directly target the relationship between the outlet and the government? Does this targeting not occur for the other outlets? Are there instances where Youtube does not tag a video and these types of comments are not present? This seems very one-way focused, and biased towards defending Al Jazeera. I do not think this has its place in the paper. In addition, it seems the wording of the three labels shown in figure 1 simply follows that of the first sentences of the three outlets’ respective Wikipedia pages. It is likely this wording is automatically derived from those sentences. I suggest the authors highly nuance, or even remove this subsection, as well as the discussion in Section 6 on it, as it seems partisan and weakens the rest of the contributions.

In 6.3, one last, important limitation is that the study was only conducted in English.

MINOR

Add white space below each table.

In 6.2, “it is becoming increasingly difficult for news media to remain [un]biased…”

Also in 6.2, “—intentionally or [un]intentionally—”

Reviewer #2: The theme of the paper is interesting, overall paper is well written and well organized. Moreover, the analysis have been rigorously performed and results are presented appropriately. I have only a few comments that are given below.

In this paper, a third party service is used for toxicity quantification that was unable to compute toxicity on 21.8% of the comments that were likely to be not written in English. To tackle this, language detection of the users’ comments can be performed earlier to toxicity analysis.

Please cite a reference to support the argument regarding manual tagging by Al Jazeera’s journalists and editors for topical keywords, if any.

A few state of the art machine learning (ML) methods can be used for performance evaluation purposes, e.g., it would be interesting to compare the performance of traditional ML methods like decision trees classifier with that of the feed forward neural network. Moreover, performance evaluation can also help in selecting a suitable ML method for analysis.

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Reviewer #1: No

Reviewer #2: No

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Topic-driven Toxicity: Exploring the Relationship between Online Toxicity and News Topics

PONE-D-19-19498R1

Dear Dr. Salminen,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

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With kind regards,

Pin-Yu Chen, PhD

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The revised version has addressed the reviewers' concerns from the previous round. I thank the authors and the reviewers for making great efforts in improving this submission. I recommend to accept this version for publication as is.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #2: All comments have been addressed

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

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

Reviewer #2: Yes

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

Reviewer #2: Yes

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

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

Reviewer #2: Yes

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

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

Reviewer #2: Yes

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6. Review Comments to the Author

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

Reviewer #2: (No Response)

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Reviewer #2: No