"Where Did All the Lesbians Go?" A Content Analysis of the Sense of Community within Lesbian Spaces on TikTok
Bibliographic record
Abstract
As physical lesbian spaces continue to diminish, platforms like TikTok have become essential for queer women seeking community. This study uses deductive content analysis, guided by McMillan and Chavis’ (1986) sense of community framework, to examine how membership, infuence, fulfllment of needs, and shared emotional connection are expressed in these digital spaces. By analyzing the top 100 comments on two videos from 11 popular lesbian TikTok creators, whose followers range from 340 thousand to 9 million, the research explores how a sense of community is fostered online. A total of 22 videos were analyzed, with comments coded into key themes. The fndings reveal how users express solidarity through mutual support and validation, often rooted in shared language and collective experiences unique to lesbian identity. Commenters navigate issues such as relationships and societal marginalization, fostering a sense of belonging. However, tensions emerge as users grapple with inclusivity and representation, particularly around race, gender identity, and the evolving defnition of lesbian identity. These discussions highlight both the unifying aspects of digital lesbian spaces and the challenges of ensuring diverse voices are heard. This research underscores TikTok’s evolving role in shaping lesbian identity and community, spotlighting both opportunities and challenges for fostering belonging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".