Non-Binary Gender Expression in Online Interactions
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Abstract (arXiv)
Many openly non-binary gender individuals participate in social networks. However, the relationship between gender and online interactions is not well understood, which may result in disparate treatment by large language models. We investigate individual identity on Twitter, focusing on gender expression as represented by users chosen pronouns. We find that non-binary groups tend to receive less attention in the form of likes and followers. We also find that nonbinary users send and receive tweets with above-average toxicity. The study highlights the importance of considering gender as a spectrum, rather than a binary, in understanding online interactions and expression.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Mar 2023.
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Citing papers per year: 2024: 4; 2025: 1.
5 papers.
- 2024Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Biascited by 14
- 2024COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communitiescited by 12
- 2024OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistantscited by 6
- 2024Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forumscited by 3
- 2025SLAyiNG: A Diverse and Community-validated Dataset of Queer Slangcited by 1
- 2025SLAyiNG: A Diverse and Community-validated Dataset of Queer Slang22 Sep 2025
- 2024Leveraging Machine Learning to Identify Gendered Stereotypes and Body Image Concerns on Diet and Fitness Online Forums4 Jul 2024
- 2024OATH-Frames: Characterizing Online Attitudes Towards Homelessness with LLM Assistants21 Jun 2024
- 2024COMMUNITY-CROSS-INSTRUCT: Unsupervised Instruction Generation for Aligning Large Language Models to Online Communities17 Jun 2024
- 2024Harmful Speech Detection by Language Models Exhibits Gender-Queer Dialect Bias23 May 2024