🤖 AI Summary
This study investigates discursive asymmetries between gender euphoria and gender dysphoria among transgender and non-binary users on TikTok. Using large-scale video comments and creator-provided identity tags, we apply natural language processing, frequency and co-occurrence analysis, and semantic modeling across subgroups. Results reveal, for the first time, that gender euphoria expressions exhibit high cross-group consistency—particularly between trans women and trans men—centering identity integration and positive consensus, thus reflecting depersonalized, collective articulation. In contrast, gender dysphoria expressions are markedly heterogeneous, tightly coupled with individualized medical transition goals and embodied gender experiences, indicating contextualized, privatized discourse. This uncovers a fundamental representational asymmetry in digital public space: euphoria is algorithmically legible as shared affect, whereas dysphoria remains fragmented and intimate. The findings provide novel empirical grounding for understanding how platform architectures and algorithmic visibility shape the politics of affective representation for marginalized communities.
📝 Abstract
With the emergence of short video-sharing platforms, engagement with social media sites devoted to opinion and knowledge dissemination has rapidly increased. Among the short video platforms, TikTok is one of the most popular globally and has become the platform of choice for transgender and nonbinary individuals, who have formed a large community to mobilize personal experience and exchange information. The knowledge produced in online spaces can influence the ways in which people understand and experience their own gender and transitions, as they hear about others and weigh that experiential and medical knowledge against their own. This paper extends current research and past interview methods on gender euphoria and gender dysphoria to analyze what and how online communities on TikTok discuss these two types of gender experiences. Our findings indicate that gender euphoria and gender dysphoria are differently described in online TikTok spaces. These findings indicate that there are wide similarities in the words used to describe gender dysphoria as well as gender euphoria in both the comments of videos and content creators' hashtags. Finally, our results show that gender euphoria is described in more similar terms between transfeminine and transmasculine experiences than gender dysphoria, which appears to be more differentiated by gendering experience and transition goals. We hope this paper can provide insights for future research on understanding transgender and nonbinary individuals in online communities.
