Which Gender is More Concerned About Transgender Women in Female Bathrooms?

People assume a level of privacy and safety when using public bathrooms. The presence of a transgender person in a public bathroom that matches their gender identity may be viewed as crossing a societal boundary, and can cause other bathroom users to question which other boundaries that transgender person might cross, raising questions of safety and privacy.

Other bathroom users, particularly those unfamiliar with transgender people, might question or protest a transgender person’s presence in the bathroom, possibly arguing that they’re not really the gender they claim to be [5, 17, 57] or that transgender people are inherently unsafe [46]. Security or the police might be contacted, or a transgender person might be accosted by members of the public in the vicinity [8]. Consequently, for many transgender people, simply using a public bathroom can be a daunting prospect [19, 26].

Laws concerning whether or not a certain transgender person is permitted to use a bathroom consistent with their gender identity are controversial and vary considerably [11, 15, 21, 22, 36, 45]. There are currently numerous news articles and opinion pieces on proposed laws designating under what circumstances transgender people may use a bathroom consistent with their gender identity. These so-called ‘bathroom bills’, predominantly from the USA, propose criteria for when it is suitable for someone to use a gendered bathroom. The content of these bills may be influenced by psychosocial versus biomedical lay theories on transgenderism [14].

Restrictive laws and regulations are usually proposed on the grounds they ‘will protect women and children’ [47, 51] and ‘for public safety and privacy’ [2]. Investigating how big of a concern this is to female bathroom patrons (whose safety and privacy is hypothetically being put at risk) is the motivation of this paper.

Regardless of their stance, online articles contribute to public awareness of transgender people, and, in particular, their use of bathrooms. Moreover, these articles, and the Internet in general, also affect how transgender identities are shaped [1, 23, 48].

Many online news articles allow for user comments to be posted, which can allow a researcher to gain insight into the topic (see [41] for more information about using the Internet as a source of sexuality data). In this paper, we use such comments to gauge public opinion on the use of public bathrooms by transgender females consistent with their gender identity.

We use a mixed methods approach: enumerating web comments for hypothesis testing (quantitative) and inspecting web comments for common themes (qualitative). We focus exclusively on transgender females (male-to-female transgender people of all ages) in this paper because the overwhelming majority of comments are directed towards this class of transgender people. We also choose to focus on opinions regarding safety and/or privacy of patrons in female bathrooms.

There is a dearth of research concerning attitudes towards transgender people separate from other LGBT subgroups [18], and, in particular, separate studies for individual transgender subgroups [59].

Previous work on public opinions of transgender people has mainly involved surveys and interviews, and are not specifically directed towards bathroom usage; see [43] for a survey. We instead take the approach of analyzing Internet comments for public opinions. We focus on a particular sub-topic, bathroom safety and privacy, as it is currently a hot topic in online news and consequently receives many user comments.

The author is aware of two previous works analyzing online data in regards to transgenderism: Westbrook and Schilt [54] observed an overwhelming focus on genitalia as the determining factor of gender: ‘opponents (of trans-inclusive policies) give penises the power to destroy the sanctity of women’s spaces through their (presumed natural) propensity to rape’ (p. 48). Krueger and Young [33] enumerated Twitter tweets containing transgender-specific hashtags, finding that 80%+ of tweets related to (positive or negative) social ideas, events, or actions (predominately Laverne Cox’s Emmy nomination), with a small percentage (around 5%) relating to bathrooms.

In this paper, a person is described as transgender if their gender (or gender identity) does not match their sex assigned at birth. A transgender female refers to a transgender person that identifies as female, and of any age. We similarly define a transgender male. A person is described as non-binary if they neither identify as male nor female. A person is cisgender if they are not transgender. We use ‘trans’ and ‘cis’ as abbreviations for ‘transgender’ and ‘cisgender’.

Because it’s less cumbersome to write ‘female’ and ‘male’ than ‘women or girl’ and ‘man or boy’, respectively, we use these terms irrespective of a person’s phenotypic sex and medical history. Thus, in referring to someone as e.g. a transgender female, we make no claims about their use of hormones, their genitals, and so on.

Quotes are used throughout this paper and, unless otherwise indicated, each is a snippet from one of the 1035 online comments enumerated in this research (paraphrased to inhibit searching and preserve users’ anonymity for ethical compliance).

The author searched the Australian version of Google News (sourced between April 29 and May 16, 2015) for news and blog articles for phrases such as transgender bathroom and manually inspected a large number of web news articles and opinion pieces for comments made in relation to safety and/or privacy in female bathrooms. Of the articles inspected, 190 contained suitable comments, from which the author collected 1035 comments. All articles were dated between 1 December 2014 and 10 May 2015 (inclusive).

The comments collected were categorized as negative and non-negative, according to whether or not the comment expresses (or implies) concern that safety and/or privacy is being put at risk. Among the negative comments were those that pertain to (a) transgender females directly violating other female patrons, and (b) transgender female usage of female-only bathrooms, or laws that facilitate such usage, enabling others to violate other female patrons.

Comments that do not contain or imply an opinion over safety and/or privacy were not included, although they might be negative, e.g. ‘I don’t want to go in the bathroom and find a man in there at the same time’; ‘They can use a tree or a fire hydrant like other animals!’; ‘We should kick that kid out of school and put him in a nut house where he belongs.’

Comments were further categorized according to the gender of the user; this resulted in six categories: cisgender male, cisgender female, transgender male, transgender female, non-binary, and unclear (when the avaiable data was not enough to provide a reasonable suspicion of the user’s gender). These gender groups were split into confirmed, where there was conclusive evidence of a user’s gender, and suspected, where there was reason to suspect a user had a certain gender, but the evidence wasn’t conclusive.

The author based her categorization of a user’s gender based on (a) usernames, (b) the content of the comment and other comments that user made, and (c) the user’s profile. In many cases, the user had a linked Facebook account, where it could be straightforward to verify the user’s gender and transgender status. If a user had a gendered username, the user did not express that they are transgender, and their user history indicated that they had often used their account for commenting on articles about topics other than transgenderism, the author took this a ‘confirmed’ gender. If a user commented predominately on transgender topics, or had several transgender-appearing friends on their Facebook page, the author suspected they would be transgender themselves. If a user expressed a surprisingly favorable view of transgender people, they were also suspected of being transgender (this errs on the side of caution when testing the hypothesis that cisgender males express more concern than cisgender females). Using terms such as ‘transgendered’ or ‘tranny’, which are currently unpopular in transgender communities, and misgendering transgender individuals led the author to suspect a user is cisgender (although, this would be interpreted in-context).

The author attempted to be as exhaustive as possible in her search. All articles that were found and had suitable and accessible comments were included. No effort was made to filter articles based on the political opinions expressed in the articles; some articles were clearly trans-positive whereas others were clearly trans-negative. Users who made multiple comments (sometimes over more than one news article) were always consistent in making either negative or non-negative comments, and so they were only counted once.

Three ambiguous user comments were omitted (e.g. a user suggested that ‘rape rates would increase’ but it was unclear who the hypothetical rapes would be against, and in what circumstance). One news article with more than 3500 comments was not able to be fully inspected as viewing a large number of these comments would cause the author’s computer to become unstable; 180 predominately negative comments came from this single source. One news article with more than 10,000 comments was not inspected to completion; 15 of the total number of comments came from this source.

Comments by Gender

To highlight which gender makes which kind of comment (negative or non-negative), we plot the sampled distribution of comments by cisgender males and cisgender females in Fig. 1. Transgender people almost uniformly wrote positive comments, so we don’t include those comments in Fig. 1.

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We further class the negative comments by confirmed cisgender males, confirmed cisgender females, and by people whose gender were unclear as ‘incidental’ and ‘causal’, as defined in Table 2. The results are plotted in Fig. 2.

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We test the stability of the key numerical observations by repeating the enumeration while ignoring any particular comment with probability 30%. This is repeated 100×, and we calculate the standard deviation of the sample obtained. In this way, we give a basic measure of how sensitive the numerical results are to omissions. The results are included within the following.

The observed proportion of negative comments is found to be around 72% for cisgender males (264 out of 366) and 46% for cisgender females (119 out of 256), differing by a factor of around 1.55 (SD 0.08). The factor remains approximately the same if we include the data from ‘suspected’ genders. This shows that cisgender males are more likely to be concerned with safety and privacy surrounding transgender females in female bathrooms than cisgender females (consistent with the well-established hypothesis that males are more transphobic than females).

This observation was, in fact, reflected in a comment by a cisgender female:

I worked with a trans woman. There were men who were concerned about her using the women’s bathroom. I told them we didn’t care; we have stalls and don’t watch each other pee.

Although not all cisgender females feel that way:

I work with a male trans and he uses our ladies bathroom, so I have to go upstairs.

Comments classed as ‘incidental’ comprise around 6% of the negative comments for cisgender males (16 out of 264) and 24% for cisgender females (29 out of 119), differing by a factor of around 4 (SD 0.9). Comments classed as ‘causal’ comprise around 32% of the negative comments for cisgender males (85 out of 264) and 21% for cisgender females (25 out of 119), differing by a factor of around 1.5 (SD 0.2). This second observation is comparable to an observation by [35], who found that 1.5× more males thought transsexuality is ‘always wrong’ in a survey of 318 university students.

These statistics show that cisgender males are not only more likely to be concerned with safety and privacy surrounding transgender females in female bathrooms, but are also more likely to express that transgender females directly cause their concerns.

The fact that cisgender males don’t normally use female bathrooms makes the observations about their heightened concerns about transgender females in female bathrooms particularly curious. Female concerns tend to be similar in content to male concerns, but are less frequently posted and phrased less intensely.

Uncovered in this research were other recurring themes, which would be interesting to investigate individually.

A transgender female using a female bathroom or a transgender male using a male bathroom is inherently wrong ‘Females shouldn’t have to share a locker room with someone with a penis’; ‘People should use the bathroom that matches their current genitalia’; ‘Any person who doesn’t know which bathroom to use, should just stay home’; ‘He is a man and should behave like one’.

These comments present a feeling as opposed to an argument. Research in this direction could investigate how these feelings change over time, as transgender people become more familiar to the general public.

Transgender identities are insignificant ‘Can I say I feel like a women and walk into the female bathroom?’; ‘The wants of a few shouldn’t outweigh the needs of the many’; ‘A dude decides one day that he wants to ‘identify’ as a female, then he can use the girl’s bathroom’.

These comments indicate a lack of public awareness of the strength of the conviction transgender people have for their gender identities. Comments such as ‘look in your pants: if you have a penis then you belong in the mens room’ overlook the fact that a lifetime of living with a penis is not enough to convince a transgender woman that she is a man. This raises the question: does awareness of the strength of transgender identities correlate with acceptance of them using bathrooms consistent with their gender identities?

Victims of assault by men will be adversely affected by transgender females using female bathrooms ‘I wonder how a women who has been assaulted would feel knowing a man could be in the bathroom?’; ‘Shouldn’t there be a ‘trigger warning’ above the locker room for those women who might have been raped, sexually assaulted, etc., before they enter the peep show?’.

In the enumerated user comments, (a) three cisgender women claimed to have been sexually assaulted by men at some point in their lives (one repeatedly) who said they would have no discomfort with sharing a female bathroom with a transgender female, (b) one cisgender woman was ‘molested as a young girl’ and said she is ‘...not worried about transgenders, only people who would use that in order to access children’, and (c) two cisgender women reported being assaulted (one by a transgender woman) and one cisgender woman reported an attempted assault, who all expressed strong concerns with sharing a female bathroom with a transgender female. This raises the question: how does this sentiment extend to the larger population?

Potential for human error Although effort was taken to minimize human error in recording, the author cannot eliminate the possibility that she skipped some relevant comments and/or articles. Comments omitted in this way should not disproportionately affect the results.

All comments that pertained to safety and/or privacy were included. We classify a comment as ‘negative’ if it expresses concern about safety and/or privacy, regardless of how intense the comment was phrased. This choice of definition almost always resulted a clear distinction as to which comments should be classed as negative, making it difficult to misclassify comments.

In some cases, the intent of the user needs to be incorporated into the classification, e.g. in the case of a rhetorical question (e.g. ‘And how often do perverts dress up as women to invade ladies bathrooms?’ would be classed as non-negative) or sarcasm (e.g. ‘I want some pervert to try to get in a bathroom my granddaughter is using’ would be classed as negative).

Sometimes, context needs to be taken into account in classifying user comments. E.g. a comment such as ‘persecuting transgender people will not make anyone safe; sexual predators look ordinary and would prefer not to draw attention to themselves’ written in response to an article about ‘bathroom bills’ would be included and classed as non-negative.

Practical constraints (financial and geographical constraints, along with the tedious and time-consuming nature of reviewing a large number of user comments) have made it infeasible for the author to recruit another suitable party to rate comments, and consequently the author performed this task herself. Nevertheless, the experiments described here are easily capable of being reproduced.

Unclear genders Many users do not provide sufficient information to either deduce their gender, or even have a reasonable suspicion as to what their gender is; these users’ genders we classify as ‘unclear’. Comments made by users with unclear genders comprise around 22% of all comments (232 out of 1035), with around 64% of these comments being negative (149 out of 232).

About 37% (55 out of 149) of the negative comments made by users with unclear genders were classed as ‘causal’. Consequently, caution should be taken when interpreting the observations in Fig. 2. It is likely these negative causal comments were made almost solely by cisgender males and females. The author cannot exclude the possibility that a large proportion of these comments were made by cisgender females, although she sees no concrete reason for this to be the case, instead expecting that these comments are male-dominated like the remainder of the comments.

User demographics Aside from gender, the author made no attempt to control for the users’ demographics (age, race, religion, sexual orientation, etc.). This was for practical reasons: in the vast majority of cases it is simply not possible to determine these demographics from the available user data.