The subtle language of exclusion: Identifying the Toxic Speech of Trans-exclusionary Radical Feminists (2022)

Abstract: Toxic language can take many forms, from explicit hate speech to more subtle microaggressions. Within this space, models identifying transphobic language have largely focused on overt forms. However, a more pernicious and subtle source of transphobic comments comes in the form of statements made by Trans-exclusionary Radical Feminists (TERFs); these statements often appear seemingly-positive and promote women’s causes and issues, while simultaneously denying the inclusion of transgender women as women. Here, we introduce two models to mitigate this antisocial behavior. The first model identifies TERF users in social media, recognizing that these users are a main source of transphobic material that enters mainstream discussion and whom other users may not desire to engage with in good faith. The second model tackles the harder task of recognizing the masked rhetoric of TERF messages and introduces a new dataset to support this task. Finally, we discuss the ethics of deploying these models to mitigate the harm of this language, arguing for a balanced approach that allows for restorative interactions.

## introduction transgender individuals are frequent targets of toxic language in online spaces @xcite . multiple approaches to recognizing such abusive language have focused on identifying explicit forms of abuse, such as using trans-specific slurs @xcite @xcite . however, not all verbal abuse directed towards the transgender community is so explicit. within those transphobic groups, trans-exclusionary radical feminists (terfs) are a community who is critical of the notion of gender, and position the existence of trans women as antithetical to "womanhood." 1 i find it increasingly harder to believe that the people saying this nonsense actually believe it. a man is a woman because he wears some lipstick and says he's a woman, but a woman isn't a woman because of biology?? some would say that lgb have already been "thrown under the bus" to accommodate an ideology that relies heavily upon gender stereotypes and "being in the wrong body." i hear there're a lot of lesbians who feel like this. guarantee they'll expect more rigorous research to debate the ethics of fancy shoes than they did for men in women's sports figure 1 : examples of harmful rhetoric by terfs which reference notions of biological essentialism in defining gender and exclusion of transgender women from sports. while offensive, we include the examples here to highlight the subtlety in their exclusionary messages. throughout the paper, all messages are lightly paraphrased for privacy. as such, the language of their attacks is frequently couched in arguments promoting women's safety and rights-nominally positive language. terf groups maintain an active presence across public social media and are often a source of transphobia online @xcite . however, their masked rhetoric is unrecognized by current models for hate speech detection, and indeed, identifying terfs in general can be difficult if one is not familiar with their lines of argumentation, as seen in the examples in figure 1 . interacting with individuals propagating these beliefs can be materially harmful and as a result, multiple transgender communities and allies have established lists of known terf accounts to help individuals block or avoid abuse. however, the recruitment of new individuals with terf beliefs as well as sockpuppet accounts make views consider it derogatory. nonetheless, our use follows current academic practice in naming (e.g., @xcite . manually keeping these lists up-to-date a challenge for mitigating their impact. in this paper, we widen the scope of abusive detection online by demonstrating a model for detecting both terfs and nuanced terf rhetoric on twitter by analyzing their tweets and community features. work in abusive language detection for social media has become more widespread @xcite , but more subtle forms of hate speech such as dog whistles are notoriously difficult to capture @xcite . terf rhetoric directly falls into this category, as it consists of a particular brand of transphobia that employs dog whistles and bad faith argumentation. prior work has only begun to address these subtle form of offensive such as microaggressions @xcite , condescension @xcite , and other social biases @xcite . our work identifying terfs and their rhetoric extends this recent line of research by filling the gap into an under-researched but important area of transphobic hate speech. we introduce the first computational method for detecting terf accounts on twitter, which combines information from user messages and network representations. using community-sourced data of over 22k users, we show that social and content information can accurately identify terf accounts, attaining a f1 of 0.93. to support identifying terf messages directly, we introduce a new dataset of gender and trans-identity related messages annotated for terf-specific rhetoric, showing that despite the challenging nature of the task, we can obtain 0.68 f1. together, these methods allow individuals to recognize and screen out the uniquely transphobic rhetoric of terfs. this paper provides the following contributions. first, little computational attention has been paid to terfs and transphobic speech in previous work within the realm of abusive content detection. our model is the first to tackle the challenge of capturing nuanced, transphobic rhetoric from terfs, and leveraging it to identify terfs on twitter. second, we introduce a new dataset for recognizing terf-specific rhetoric, allowing the community to expand current efforts at combating abusive language. finally, acknowledging the dual use of nlp @xcite , we consider the ethics of deploying these technologies in the risks and benefits of censuring versus allowing engagement with terfs, arguing for a balanced approach that facilitates restorative justice. ## terfs in online spaces feminist ideals aim to promote women's rights and mainstream feminism is considered inclusive of transgender women @xcite . however, a small number of individuals claiming to be feminists have taken an opposite stance, arguing for transphobic views that push for biological essentialism and criticizing the notion of gender @xcite . this group was given the name "transexclusionary radical feminists" or terfs as a way of separating their views. drawing in part upon feminist arguments in @xcite , terfs argue that gender derives fully from the biological sex, which is dependent on a person's chromosomes and thus is binary and immutable @xcite ; it follows in their biological reductivist reasoning that a transgender woman is a man. as a result, terfs frequently make claims seeded with anxiety about the encroachment of transgender women into women's spaces and rights (e.g., participation in sports or use of restrooms), as well as the need for biological tests of gender @xcite ). 2 for many terfs, their rationale is embedded with real but misdirected fear of violence against and subjugation of women. regardless, such harmful rhetoric directly marginalizes and excludes transgender women @xcite , often invalidating their very existence. these arguments frequently follow the subtle language of microaggressions (sue, 2010, ch.2). terfs themselves are also not a monolithic bloc; individuals may vary in their stances towards transgender people, from claiming to openly support them as a separate group to radically opposing them and arguing such identities themselves are flawed. while all such attitudes are harmful, this range suggests that some viewpoints could be changed. less prevalent in the united states and canada, terfs within the united kingdom hold an unfortunately mainstream position within feminism @xcite , with a notable proponent being j.k. rowling @xcite , author of the harry potter series. terfs are present on multiple platforms; terfs maintained an active community of over 64k users on the r/gendercritical subreddit, until june of 2020, after which it was banned by reddit for the promotion of hate speech. the presence of terfs in online communities represents a significant risk to transgender individuals, as they perpetuate targeted harassment and doxxing. online spaces are particularly critical for transgender individuals due to their role in facilitating the transition experience @xcite and seeking social support during the coming out process @xcite . as some individuals may not have publicly come out to family and coworkers (but do so online, potentially anonymously), targeted harassment poses risks for some individuals @xcite . potential interactions between terfs and transgender individuals can further marginalize individuals and reduce the perceived support. ## a dataset for recognizing terfs as frequent targets of abusive language, transgender individuals and their allies have curated lists of known terf users on twitter in attempts to mitigate the harm they cause. these user lists form the basis for our dataset, described next. ## building a terf classifier to recognize terf users, we use a multi-stage approach that combines information from individual messages on topics discussed by terfs with social features representing who they follow. following, we describe the three stages: how we (1) recognize topics closely related to terf rhetoric, (2) identify individual messages likely to come from terfs, and (3) combine textual and social features to detect terf users themselves. ## recognizing terf rhetoric when making transphobic statements, terfs employ regular arguments that delegitimize the status and inclusion of transgender women in the definition of woman. while recent work has aimed to identify explicit slurs used against transgender individuals @xcite , the terf rhetoric is more subtle. however, the high performance of our signal classifier ( §4.2) indicates terf users can be accurately identified when discussing transgender topics. now, we test whether we can explicitly recognize which statements contain harmful terf rhetoric. we first create a topically-focused dataset of transgender-related content and label messages by whether they contain a terf rhetoric, and then use this corpus to train classifiers. ## values and design considerations the computational tools developed in this paper in §4 and §5 facilitate the detection of terfs and their rhetoric. to what end should these tools be used? the majority of antisocial or toxic language detectors are used punitively for censure or removaluses of toxic speech are removed from public visibility and the transgressing individuals are potentially subject to temporary suspensions or even account removals. given that at their core, many terfs are feminists who are primarily concerned with women's rights and safety (albeit mistakenly latching onto a biological essentialist definition of "women"), we view the application and deployment of our tools as an ideal ethical case study for alternatives to the traditional punitive uses of abusive language detection. as nlp moves from focusing on the language of bad actors to examining nuanced discourse in a gray area, we must rethink how our methods are deployed and what the ultimate goals of such tools are: reconciliation and rehabilitation, or potential radicalization through alienation. due to the political nature of a terf detector, it is worth critically examining such work through contemporary lenses of "cancel culture" @xcite and restorative justice @xcite . this work intends to provide a useful tool allowing marginalized people in the trans community to curate their online experiences and avoid doxxing and harassment at the hands of terfs. however, examining its impact could raise concerns of censorship or evoke the echo chambers of algorithmicallyconstructed facebook feeds-which we explicitly acknowledge and seek to avoid. "cancel culture" is a contemporary form of ostracism that straddles online and real-world spheres and often leads to material loss for the "cancelled" @xcite . the phenomenon is largely punitive and, combined with other forms of online censorship such as deplatforming, generates further polarization; it pushes people away to be radicalized in remote spaces. online moderation tools have typically relied on these types of actions to remove content @xcite . while community-level bans have been effective at reducing harm without creating spill-over into other communities @xcite , such actions still run the risk of removing the possibility of further engagement that leads to a change in underlying views. thus, we do not label people as terfs in order to silence or "cancel" them. rather, we consider it a tool to better engage, understand, and ultimately find a path to reconciliation. we reiterate that the methods outlined in this paper should not supersede human judgment, but rather be used in tandem to best inform the user. it is worth being cautious of the fact that people take ai models to be objective arbiters when in reality, they can and do embed bias in many facets (e.g., @xcite . such a system should not be viewed as the end-all-be-all in decision-making. the ideal use-case of terf detection should be grounded within a framework of restorative justice @xcite ; instead of punitive retribution, we seek rehabilitation through mutual engagement, dialogue, and consensus. users should be able to decide how to engage upon encountering a terf guided by an assessment of terfs stance (e.g., transphobic severity) and whether they are equipped and able to put in the labor of understanding and addressing their fears. as potential next steps for deploying our models in a manner to minimize risk, @xcite and @xcite have proposed visual mechanisms for displaying "social signals" of other individuals on social media to create an informed decision about potential interactions; our tool could easily lend itself to such mechanisms by identifying users by their likelihood of being a terf and also, if the user is willing, to show content our model has identified as being terf rhetoric to assess their stance. while promoting interactions between the transgender community and terfs poses risks, we retain some optimism for establishing shared common ground to facilitate dialogue. indeed, as our topic model showed, the bulk of terf users' message is not about transgender issues and much of this content overlaps with that written by transgender women; for those willing to engage, new nlp methods could be used to (i) identify particular nonconfrontational topics to foster an initial dialogue, (ii) suggest potential counterspeech, building upon recent work on counterspeech for hate speech @xcite @xcite , and (iii) analyze their statements to identify those terfs whose stances signal they could be open to change @xcite . ## conclusion online communities serve essential roles as places of support and information. for transgender individuals, these spaces are especially critical as they provide access to accepting and supportive communities, which may not be available locally. however, the public forums of social media can also harbor less than welcoming users. transexclusionary radical feminists (terfs) promote a harmful rhetoric that rejects transgender women as women, pushes an agenda that reduces gender to biology, and seeks to invalidate transgender women in policy and practice. as a result, transgender individuals and their allies have adopted technological solutions to limit interactions with terfs by manually curating block lists, which require frequent updating and currently rely only on self-reporting to recognize those users who pose harm. this paper introduces new datasets and models for supporting the trans community through automatically identifying terf users and their rhetoric. we present a new multi-stage model that identifies salient themes in terf users' content and show that these signals, when combined with social network features, result in a highly accurate classifier (0.93 f1) that reliably identifies terf users with minimal risk of mistakenly labeling trans-friendly users as terfs, despite sharing similar content themes. further, we introduce a new dataset for directly identifying the often-subtle rhetoric of terfs and show that despite the challenging task, our model can attain moderately high performance (0.68 f1). together, these two tools can aid the trans community in mitigating harm through preemptive identification of terfs. all data, code, models, and annotation guidelines will be available at https: //github.com/lu-christina/terfspot. by peers and increased mistrust. however, this risk is offset, in part, by the risk of not developing such technology. transgender individuals actively and manually identify terf users to minimize their interactions with such toxic content. however this identification is labor intensive and (i) exposes users to terf content, increasing harm and (ii) is likely to miss some users due to the scale of finding terf users on social media. as a result, inaction increases the harm to transgender users. recognizing this trade-off, we have performed additional analyses to minimize the risk of false positive classifications of users as a terf, showing that our model has a low false positive rate ( §4.3). who should be on a block list? our models are trained on community-curated block lists, with a goal of helping individuals identify others who might be engaged in harmful terf rhetoric. yet, it is worth considering whether such actions potentially perpetuate harm by minimizing discourse, increasing polarization, or even serving as a "marker of success" for antagonistic users to aim for. we explicitly do not advocate automatically including any user on a block list and, instead, as outlined in §6, argue for more nuance and consideration in how users apply this technology. we view an ideal application of our model as one that allows each person to define their own comfort level in exposure and engagement in an informed manner. our tool can serve as a social signal to help others guide their decision but should not be taken as ground truth for blocking anyone. dual-use risks many nlp methods, including those presented here, have dual-use for good and bad purposes. our models could be used to deployed to identify and "cancel" terf users, cutting them off from the larger social media community. further, terf users could use our models adversarially to test how their own accounts are classified and systematically change their behavior to avoid future detection. yet, in our setting, the technology offers substantial benefits for a marginalized group, transgender individuals, who have been overlooked by nlp methods for identifying transgender-targeted content. our models augment their ability to identify terf users and use this knowledge as they see fit. given the harm faced by transgender individuals, we view the benefits as substantially outweighing risks.