Signaling the Intent to Change Online Communities: A Case From a Reddit Gaming Community
Bibliographic record
Abstract
This study builds on existing research about churn and community movement, examining if language use on Reddit can be used to determine if people signal their intent to relocate to a new community before they actually do so. Using a computational and semantic approach, we studied the subreddits for the game series Fallout at the time Fallout 76 ( FO76) was released to see if the users of the Fallout 4 ( FO4) subreddit signaled how they would react to the new subreddit. The main difference we found was that those who stay in the FO4 subreddit or use both subreddits on average post more often and create longer posts than those who move to the FO76 subreddit or leave. This adds further evidence to support theories about community as communication, and we suggest this finding can help online community managers identify which users may be about to leave the community, aiding retention and the overall health of the community.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".