A Discourse of Hate: A Content Analysis of Responses to Queer Representation in Online Social Media
Bibliographic record
Abstract
This study examined public responses to Elle Magazine’s Facebook post titled “30 LGBTQ+ Celebs You Really Should Know” to reveal common rhetoric towards lesbian, gay, bisexual, transgender, queer and/or questioning, intersex, asexual, and two-spirit (LGBTQIA2S+)-based discussion on social media. A content analysis of 100 comments uncovered several themes in negative reactions to queer representation in the media sphere including: preferred ignorance, lack of understanding, blatant distaste, religious bias, the view that queer identity is unnatural, an illness, an identity crisis, a trend, and a trauma response. Of the 100 comments examined, 86 responses were negative. The most prevalent theme was preferred ignorance, where respondents favoured keeping queer representation out of public discourse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".