Free Speech and Safe Spaces: How Moderation Policies Shape Online Discussion Spaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do moderation policies affect online discussion? This article analyzes nearly a quarter of a million anonymous comments over a 14-month period from two online Reddit forums matched in topic and size, but with differing moderation policies of “safe space” and “free speech.” I found that in the safe space, moderators removed significantly more comments, and authors deleted their own comments significantly more often as well, suggesting higher rates of self-censorship. Looking only at relatively low frequency posters, I found that language in the safe space is more positive and discussions are more about leisure activities, whereas language in the free speech space is relatively negative and angry, and material personal concerns of work, money, and death are more frequently discussed. Importantly, I found that many of these linguistic differences persisted even in comments by users who were concurrently posting in both subreddits. Altogether, these results suggest that differences in moderation policies may affect self-censorship and language use in online space, implicating moderation policies as key sites of inquiry for scholars of democratic discussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 it