Beyond the Binary: Analysing Transphobic Hate and Harassment Online (2025)

Abstract: Online communities provide support and help to individuals transitioning gender. However, this point of transition also increases vulnerability, coupled with increased exposure to online harms. In this research, we analyse a popular hate and harassment site known for targeting minority groups, including transgender people. We analyse 17 million posts dating back to 2012 to gain insights into the types of information collected about targets. We find users commonly link to social media sites such as Twitter/X and meticulously archive links related to their targets. We scrape over 150,000 relevant links posted to Twitter/X and their archived versions and analyse the profiles and posts. We find targets often tweet about harassment, popculture, and queer and gender-related discussions. We develop and evaluate classifiers to detect calls for harassment, doxxing, mention of transgender individuals, and toxic/abusive speech within the forum posts. The results of our classifiers show that forum posts about transgender individuals are significantly more likely to contain other harmful content.

## introduction life transitions can be tricky, and many people turn to online resources to help navigate this change @xcite . while online communities may provide help and support @xcite , there is also the potential for users to experience unintended consequences. engaging online also exposes people to online harms, making them even more vulnerable during difficult times. one major life change that some people face is gender transition @xcite . to design better tools to help users navigate life transitions, we need to first understand the risks being faced. in this research, we explore online hate and harassment directed towards those who have under-gone gender transition. we use data from a hate and harassment site included in the extremebb corpus @xcite of posts scraped from extremist forums. the forum was chosen due to the coordinated harassment of minorities, including transgender people. users on this site commonly link (include a url in their post) to the social media profiles (usually twitter/x foot_0 ) of targets, often using an archive service. we find instances of crossposted links to social media and archive sites and scrape additional data from the urls. to minimise the quantity of hateful content we view and make it feasible to analyse such a large amount of data, we develop classifiers to predict which posts relate to calls for harassment, transgender people, contain doxxing, 2 and contain abusive/toxic content. archiving services take snapshots of a website, preserving it exactly as it was when the snapshot was taken. this can preserve posts that are later deleted, or accounts that are made private. we collect the archived versions of twitter/x and compare these with more recent data. by evaluating the changes between archived and current versions, we can identify what changes have taken place, such as the profile being made private, profiles being suspended, tweets being deleted, or profiles being deleted. we use these three main data sources to address the following research questions: rq1 is there evidence of displacement away from twitter/x towards other sites, such as mastodon, blue sky, or threads? rq2 how do victims respond to harassment? do they make their profiles private or delete posts? do they change handles? rq3 is harassment moderated? rq4 how do attackers select targets? rq5 are transgender individuals more likely to be doxxed or harassed compared to nontransgender individuals? we observe recent changes to online platforms, to understand how moderation activities and the spaces where abuse is occurring change over time. we analyse the distribution of links on the forum and archive sites to assess displacement effects as users leave websites, or moderation efforts change. this provides insight into how targets are doxxed, what information is being spread, and where. we make several contributions. we train four binary classifiers to detect calls for harassment, doxxing, transgender targets, and abusive or toxic speech in the forum posts. overall, we find an upward trend in the presence of harmful content over time. we scrape over a decade worth of archived links from archival sites and extra content from those linking to twitter/x to understand the user behaviour and the types of links shared. we analyse the contents of the archived tweets. we analyse the changes in the profiles over time. we also find that most profiles belong to relatively small creators. we provide an overview of related work in §2. §3 describes our dataset and methods. we evaluate classifiers in §4, and in §5 provide our findings. we discuss our findings in §6, including potential future research directions. our conclusion are found in §7, followed by the limitations in §8. tables and figures are provided in the appendix. ## research methods in this section we explain our research methods. we start with an overview of the ethical concerns we considered when designing the project. for our data collection phase we collected the links posted in the extremist forum and scraped further data from x and archival sites. an overview of the data processing pipeline can be seen in figure 2 . ## classifier development 4.1 manual annotations to evaluate and develop classifiers for large-scale analysis of the data, three annotators manually label a subset of the forum posts. we label all posts as positive or negative for the four categories (calls for harassment, doxxing, transgender target and toxic or abusive content) outlined in the annotation guidelines provided in the initial set of 300 threads was randomly selected from all threads on the forum. we annotated the first five posts in each thread (or fewer if there were less than five posts), totalling 1,491 posts. we annotate the first posts from a thread as those tend to provide the most context, and annotating multiple posts together helps to understand the context. we find the first posts tend to contain the initial call for harassment and information about the target. we met to discuss disagreements, which were mainly decided by majority vote. two annotators also completed a second round of annotations to improve classifier performance by annotating another 100 posts with the lowest confidence in the classifier results out of the forum dataset. ## discussion we explore the harassment and doxxing posted on hate forums with a focus on how it affects transgender people. the most popular social media site linked to is twitter/x. this may be because on twitter/x users often post using accounts linked to their real world identity (unlike reddit, where users tend to be anonymous), and posts are by default public (unlike facebook, where you often need to friend someone before seeing their whole profile and post history). however, personal information shared about targets, such as full names, birthdays, and names of family members, is likely collected from these sites using publicly available information. targets on twitter/x mostly respond by selfcensorship (deleting tweets), but do not tend to private or delete their accounts. we find little evidence of users changing their username on the platform, perhaps because twitter/x still redirect links to posts to the new handle. while we find evidence of twitter/x moderating content posted on the platform, it is difficult to evaluate the type of content they moderate, as we do not know which post(s) caused the suspension, and if they belong to the subset of archived posts. moderation may also incorrectly flag accounts, as @xcite find minority groups experience more frequent content removal despite following site guidelines. existing classifiers trained to detect doxxing and calls for harassment do not perform well across domains, hence we train our own classifiers. existing classifiers are trained on relatively short posts, while many forum posts are essay-length. the hateful language also contains a lot of slang, particularly words meant to be insulting or derogatory to minorities. this makes building generalisable classifiers for use on many different platforms difficult. compared to non-transgender related content, forum posts relating to transgender people are significantly more likely to including doxxing and toxic content. the proportion of both is much higher in the first posts of the threads, which tend to be longer-form dossiers on the targets. while transgender individuals are more likely to be targeted, there is little evidence of users' displacement from twitter/x to other platforms. this may point to the importance of online spaces to minority groups. @xcite suggest for minority groups such as lgbtq+ youth, offline contexts are often hostile and unsafe, therefore online communities are an important place for self-expression and exploration. however, increased exposure to online spaces can also increase the likelihood of experiencing online harms. therefore, there is a need to help the members of queer support communities participate in a safe way. the solution should not be to avoid online spaces altogether. rather than discouraging the lgbtq+ community from being open on social media, this should be encouraged and normalised, and moderation efforts should recognise the types of harassment they are subjected to. ## conclusion we analyse a large hate and harassment forum. twitter/x is by far the most commonly archived site, accounting for almost half of archived links. we do not find evidence of displacement to alternative social media sites such as mastodon, threads, or bluesky. comparing the archived and current states of twitter/x links allows us to analyse changes over time. almost half of posts are no longer available as they are deleted or the accounts are private or suspended. tweets contain many different topics, including harrassment, storytelling, popculture and queer/transgender discussions. profile biographies include common themes, such as hobbies or occupations, gender and sexuality-related terminology, preferred pronouns, and pride flags. we manually annotate posts to train four classifiers to detect calls for harassment, doxxing, transgender targets, and toxic/abusive content in the forum posts. the perspective api performs poorly on toxic forum data. we use our classifiers to automatically label all 17+ million forum posts. posts mentioning transgender people are significantly more likely to contain calls for harassment, doxxing, and toxic or abusive speech, and these categories are most likely to be found in the first post of the thread.