Analyzing Binary Relationships of Identity Labels Using Distributional Semantic Models

Following the shift towards quantitative, corpus-based analysis in queer linguistics, I examine the usage of identity labels to explore the binary relationships and predicted normative effects in the case of the online community r/lgbt, a subreddit dedicated to minority identity labels and discussion. I analyze the distribution of the most frequent identity labels of the subreddit in a 2-year period with distributional semantic models, vector-based matrices that capture word distributions as numeric representations, showing evidence for various binaries that co-construct each other within the corpus. Additionally, I utilize concordances and collocations to examine the discourses surrounding gender and sexuality in the comments and submissions subcorpora, showing a more queer-aligned perspective in the former and a label-searching perspective in the latter. Finally, the results from these techniques demonstrate the overall complex relationships between the many types of labels currently in use and between the subreddit users and their feelings about adopting specific labels to describe their identities.