PONE-D-21-07964

Faces in the Crowd: Twitter as Alternative to Protest Surveys

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?

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: I Don't Know

Reviewer #2: N/A

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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: Yes

Reviewer #2: Yes

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

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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: The authors of manuscript PONE-D-21-07964 propose a novel method for analyzing political leanings and demographics of protest participants by using twitter posts. For this, the authors located people who posted in close proximity to a protest route and analyzed these tweets in terms of political leaning, age, and gender. Results are compared to on the ground surveys and a random sample of tweets using hashtags alone. While the study makes an important contribution to advancing the understanding of the composition of protest crowds, I have a number of concerns, which I will now list below:

1. While the historical example in the beginning is a nice introduction to the topic, it could be shortened at bit.

2. The related literature section focuses a lot on surveys on protesters done on the ground, and gives only little information on related studies that used twitter hashtags to study protesters. The section could be improved by discussing the latter in more detail, elaborating on potential difficulties with this design. In doing so, the reader could understand even better why the novel method is superior to past studies that only used twitter hashtag data.

3. You state that by looking at the hashtag alone, you could not differentiate between 1) actual participants on the ground; 2) online supporters only; 3) online opponents only; 4) online commentators only. Using geo-tagging is an efficient way for determining whether a user has actually posted in close proximity to the protest route. However, how can you be sure that the user is not an on the ground commentator or an on the ground opponent? Please explain whether you were somehow able to control for this.

4. The number of tweets in the final samples are not included in the main text. Adding that information would add to the understanding of your procedure. In general, it is not obvious from the text how exactly the original sample of more than 8.6m Tweets was reduced to the final numbers of n = 916 for the geolocated and n = 922 for the photo-coded samples. Please explain this in more detail.

5. In the discussion, you state that the analysis was limited to the information users made publicly available. Can you provide more details about what public information was used exactly, and what shares of users could not be included because of this?

6. The benchmark sample was drawn at random from users who used the identifying hashtags. The two datasets that were used for this collected tweets over longer time periods, and not only on the day of the protest. For the geo-located sample, however, I understand that only tweets that were posted on the day of the protest were used. Wouldn’t the two methods be more comparable if for the random sample, too, only tweets that were posted on the day of the protest would have been considered?

In summary, I believe this study is well written and makes an important contribution to the study of protest participants. I would like to thank the authors for their hard work and wish them all the best for their subsequent steps.

Reviewer #2: The paper seeks to fill a very specific hole in our methodological knowledge: the degree to which Twitter samples (and, perhaps by implication, similar digital trace data) approximates traditional survey methods when seeking to understand protests. I think it is intriguing, and broadly of substantial use to those who research online and offline social movements, and is deserving of publication. I have some suggestions of ways in which the manuscript might be improved.

The use of the term “revolutions” is a bit confusing in places, as it is used differently across fields. E.g., in line 37, I suspect the authors are seeking a point that is closer to questions of tipping points toward mass violence or direct action, rather than a full-scale overthrow of the existing order, which is implied by the term “revolution.”

Leaving aside the difficulty of geocoding tweets more broadly, the authors should note whether they sought to categorize those who were incidentally in the location of protests. Large-scale protests are often in crowded urban environments, and those tweeting may be affected by the protests while not willfully participating in them. Naturally, whether those who are so caught up in protests should be included in a sample is a question of the research design and the research question at hand. But the assumption that the samples within a km of key central positions of the protests seems like it would catch a lot, most of the routes running through the densest business districts in each city, by design.

Footnote 9. While it is reasonable to assume removal by Twitter was largely of bot accounts, it also seems likely that non-bot accounts were removed, and likely such removals weighed heavily toward the conservative side. By your own reasoning, we might likewise assume that tweet or account deletions were more likely to be by conservatives wishing to disassociate themselves with a “losing” political movement at some point after the 2019 elections.

It’s a bit easy to lose track of the process here. The figure in the appendix helps a bit here, but I should be able to easily discern this flow in the text as well. Am I correct in understanding that the hashtag sample was your starting point and the geolocated and photo samples were filtered from the hashtag (“random”) sample? How and when was the random sample obtained? Given the flat number, how was it sampled?

The report of missing ideological scores (line 215) feels out of place given the lack of representation in the text of the n of the sample size. The percentages in Table 1 make this clearer, but the n for each of the samples should be highlighted early on (and likely listed in Table 1 as well, as total n for each sample). Relatedly, am I to assume you assigned an ideological score with any number of follows of the ideologically inflected accounts more than zero? So that if someone, for example, follows Barack Obama, but no other listed account, they are coded (presumably as liberal)? I can follow this up in the cite, but it feels dangerous.

The use of in-protest survey data as a comparator is perfectly reasonable, but calling these “ground truth” (ln 276) is problematic for many of the reasons you have already noted earlier in the manuscript. Protest survey data come with their own significant biases. One might make some guesses as to systematic error here that run pretty close to things like demographic data. Even in a relatively safe setting, women may be less likely to answer unsolicited questions by someone approaching them in public, for example. But more broadly, these are multiple attempts to ascertain a ground-truth that each approach is attempting to approximate. Comparing the multiple attempts against one another is natural, but assigning the survey data as the gold standard would require you to more clearly indicate why you are assuming this to be the case.

I appreciate the ethical note, particularly in the extended version appendix B, which is thorough and well-reasoned.

I feel like this drops things off rapidly at the end. This may be a matter of the brevity of the report. However, the question of (for example) differences in age could be explained in multiple ways: most especially, differences in age distribution among twitter users (or SM users mid-protest, more specifically) or a classification model that introduces systematic errors in approximating age. The latter could be ascertained by surveying twitter users for their age, either online (though tracking twitter users to survey them can be tricky and invasive) or by surveying protestors in person to ascertain whether they are tweeting (and potentially linking samples directly). In any case, it would be very helpful for the discussion to open up possibilities of extending the work undertaken thus far.

Overall, the alignment between appendices, labelled numerically in the manuscript but then alphabetically in the separate appendix document, is confusing and should be revisited.

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Reviewer #1: No

Reviewer #2: No

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Faces in the Crowd: Twitter as Alternative to Protest Surveys

PONE-D-21-07964R1

Dear Dr. Frey,

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.

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Barbara Guidi

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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

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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 #1: Yes

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

Reviewer #1: 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 #1: No

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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 #1: 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 #1: Dear Authors,

Thank you for submitting your revised version of the manuscript. All comments have been addressed adequately and in much detail. The manuscript has improved greatly and I therefore regard it as acceptable for publication.

Kind regards

the Reviewer

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Reviewer #1: No