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 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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".