By Vik Liv Nielsen, Junior Researcher
This project seeks to research what form transphobia takes on the social media platform X, and how this affects the mental health of transgender people. The focus of this semester has been on the former, as the research has mainly consisted of scraping and analysing certain transphobic hashtags. For the sake of this research, transphobia will be classified as any negativity and hate targeted at transgender individuals or the community.
X, formerly known as Twitter, was acquired by Elon Musk in 2022. Since then, right leaning values have taken over almost every aspect of the platform. Many I have spoken to about my project in passing, have stated how they decided to delete the app off their phone after Musk’s acquisition, as they grew tired of the negativity they were seeing on their feed. As well as this, I have personally noticed a rise in far-right content being shown to me, even when browsing the feed that theoretically should only show me content from the people that I follow. This led me to the decision that my research of transphobia should focus exclusively on X.
Preliminary Research
The topic of being transgender on X has been explored by other researchers before me. However, most of this research is quantitative, focusing mostly on the number of hateful posts. Other than this, much research into transgender issues on X exclusively delves into health issues expressed by transgender individuals.
Lack of information about transgender health issues has led some research to characterize said issues through the use of social media. Through the scraping of posts, it can be concluded that certain health issues are discussed, healthcare services and transgender healthcare being the most common (Karami et al., 2018). Krueger & Young’s (2015) project on the use of X as a tool studying transgender needs shows that many transgender individuals use X to discuss health and social needs.
From my research there seems to be a distinct lack of information about the general themes of the transphobia seen on X. The closest I could get to this subject was the mapping of homophobia and transphobia on social media in general (Sánchez-Sánchez et al., 2024). This found that there is a rise in papers detailing homophobia and transphobia between 1997 and 2022. This, however, is still largely quantitative, and does not analyze the type of transphobia seen on social media, or more specifically, X.
Besides reading articles on the subject, I spent a few hours on X researching several different hashtags. I wanted to figure out which hashtags had the most metaphorical meat on them, so that I could learn the most about the transphobia seen on X and, the common themes within the topic. I quickly realized that the two hashtags trans and transgender had completely different rhetorics. The trans hashtag seemed to mostly be used by transgender individuals to identify themselves and others, mostly talking about positive topics or sharing in-jokes from the community. Transgender, on the other hand, was mostly used to spread negativity about the transgender community, and was very rarely used by transgender people themselves.
Where I had expected the TERF hashtag to be similar in the way of trans, that being individuals using it to describe themselves, it was actually more of a mixed bag. While self proclaimed TERFs did use the hashtag to identify themselves and others, transgender people and allies used the tag to criticize the TERF movement as well.
Scraping X, and the Difficulty of Analysing Hateful Material
Having done some light research on a few different hashtags on X, I decided I wished to scrape three hashtags; Transgender, TERF and JKRowling. The final hashtag was chosen as there seemed to be a lot of transphobic activity surrounding the Harry Potter author.
My initial thoughts were to either scrape manually or through the use of RStudio, as I have done both in the past. I was more inclined to do the former, as my experience with RStudio was not the greatest. Doing the scraping manually, on the other hand, is rather time consuming and therefore limits the amount of posts one might want to scrape. I personally considered doing between 25 and 50 posts per hashtag. While voicing my concerns about doing the scraping, another researcher suggested I use 4Cat for my project. This proved to be exactly what I needed, and I was able to quickly scrape roughly 100 posts per hashtag on November 13th 2024. This scraping was done with a fresh user profile and in a research browser, meaning the data was not prone to bias from my personal scrolling.
After scraping came the daunting task of actually analysing the data. I had initially decided to do systematic, thematic analysis, by coding every single tweet and finding themes through those. However, I hit a roadblock that seemingly everyone but myself had seen coming. That being the fact that analysing 306 hateful posts is heavily draining for one’s mental health. As such, I used the play fair on December 4th to discuss possible other ways of analysing my data. My wish was to make the task less draining, while at the same time being able to keep my analysis as qualitative as possible. After many good discussions on methods, I decided to stick to thematic coding, but doing it episodically instead. I would do this by picking three to five posts, and doing a deeper analysis of these, as well as cross-examining them to find themes. I chose the posts based on common throughlines I had seen in similar posts on the given hashtag, as well as making sure they were not too negative.
Found Themes from Analysis
Public Figure.
Public Figure refers to when a public figure was mentioned in one way or another in the post. The most prominent public figures were Donald Trump, Elon Musk or J.K. Rowling. The mentioning of them was often in regards to something they had said or done, and the poster would often praise or show general agreement with them. If a public figure of more leftist standing, for example Kamala Harris, was mentioned, it was most often in order to condemn them for saying something positive about the transgender community.
Protecting Children
The topic of children, and mostly how they’re meant to be protected, was often brought up. Children were often mentioned in regards to gender affirming surgeries being performed on minors. Other mentions were made commenting on Trump’s wish to ban any talk about gender and sexuality in schools.
Similarly, transgenders are often condemned for supposedly wanting to groom or assault children. According to this discourse, both transgender individuals and transgender ideology are a danger to children.
Woke
Woke seems to be a general buzzword for the right-leaning users of X. When in relation to transgenderism, wokeness is what has convinced certain people that they are the wrong gender even when they are not. Therefore, wokeism must be destroyed for the betterment of the world.
Mental Illness
The topic of transgender individuals being mentally ill comes up a lot when analysing transphobia on X. Often the very state of being transgender is described as mental illness, however it is often also discussed how transgender people are not actually transgender, moreso they are just depressed or anxious and seeking attention.
TERF Rhetoric
TERF Rhetoric describes the common talking points that are often grouped together with the TERF hashtag or TERFs in general being mentioned. TERFs separate the sexes as being innately one way, and therefore transgender people cannot exist. According to TERFs, men are inherently evil and aggressive, whereas women are pure and weak. Therefore, it is impossible for transgender women to understand or in any way become like a woman, as the male traits will always follow them. Transgender men are seen as either traitors to their gender, or women that have misunderstood themselves.
All this culminates in an unclear picture of what a transgender person is to those that post transphobia on X. Transgender people simultaneously do not exist, are simply mentally ill, or are dangerous groomers on the hunt for children. All of this seems to be based on what they have been told by public figures, and is rarely based on scientific evidence.
Next semester I will use the data gathered as information for a workshop and a focus group, both being done with transgender individuals that use X.
Bibliography
Karami, A., Webb, F., & Kitzie, V. L. (2018). Characterizing transgender health issues in Twitter. Proceedings of the Association for Information Science and Technology, 55(1), 207–215. https://doi.org/10.1002/pra2.2018.14505501023
Krueger, E. A., & Young, S. D. (2015). Twitter: A Novel Tool for Studying the Health and Social Needs of Transgender Communities. JMIR Mental Health, 2(2), e16. https://doi.org/10.2196/mental.4113
Sánchez-Sánchez, A. M., Ruiz-Muñoz, D., & Sánchez-Sánchez, F. J. (2024). Mapping Homophobia and Transphobia on Social Media. Sexuality Research and Social Policy, 21(1), 210–226. https://doi.org/10.1007/s13178-023-00879-z