The implications of account suspensions on online discussion platforms
Bibliographic record
Abstract
This study explores the impact of temporary account suspensions on users' engagement in online platforms. Using observational data obtained through a collaboration with a prominent online discussion forum in Asia, we conduct empirical analyses that are guided by regulatory focus theory and reactance theory, and we use both propensity score matching and a difference-in-differences regression analysis to uncover insights. We find that suspended users post less frequently after they experience temporary account suspension, but that these users create longer content compared to those users who did not face account suspension. We also find that user characteristics (e.g., platform tenure, prior suspension history) moderate the impact of suspensions on content length and volume. Further, mechanism analyses reveal that content posted by users who experienced temporary account suspension receives more negative reactions from the community after the suspension, even when the content does not violate platform's content contribution guidelines. As a result, suspended users are more likely to leave a platform after the suspension. Our findings contribute to the literature that explores the effects of temporary suspensions on user-generated content management, as well as offer practical insights for platform managers who develop and enforce content moderation policies. • How do users who are temporarily suspended change their behavior? • We find that they post less often but longer content post-suspension • User tenure and prior suspension history moderate the impact of account suspension • Stigma from suspension may drive suspended users to leave the platform
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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 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".