From warnings to bans: The role of social networks in the severity of sanctions
Bibliographic record
Abstract
This study examines the influence of social networks on the severity of sanctions in an online hacking forum, using a leaked dataset containing private interactions, reputation points, and administrative actions. Applying social identity theory, power structure, and social capital concepts to social network analysis, we find that members who committed spam, lacked bidirectional relationships with admins, and were less integrated and influential were more likely to be banned than warned. Our findings highlight the significant role of social ties and individual behaviors in determining sanctions, offering new insights into the dynamics of illicit online communities. • Social network structures impact the severity of sanctions in online hacking forums. • Members linked to administrators are less likely to be banned, showing the role of social ties in conflict resolution. • Higher engagement, clustering and closeness centrality lead to warnings rather than bans. • Committing spam increases the likelihood of being banned, highlighting its severity over other infractions. • The study highlights social capital's role in self-regulating illicit online markets.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".