Testbanor

Testbanor is a term used in discussions of online community moderation to describe a framework for evaluating ban decisions. It denotes a systematic approach to testing, validating, and comparing different banning policies before they are deployed in production environments. The aim is to anticipate costs and benefits, including fairness, effectiveness, and user impact, rather than relying solely on post hoc observations.

Origins and scope: The name is a portmanteau of "test" and "ban" and is used to distinguish studies that examine whether a ban policy achieves its stated goals from those that merely document ban counts. In practice, testbanor is invoked to frame policy evaluation as an experimental or quasi-experimental process.

Components: A testbanor framework typically includes policy specification (clear definitions of offenses and corresponding sanctions), a data model (records of violations, appeals, and outcomes), a simulation engine or study design (for controlled comparisons), and a set of evaluation metrics (precision and recall of bans, false positives/negatives, recidivism, user welfare, and perceived legitimacy).

Methodologies: It may employ A/B or multi-armed experiments, factorial designs, or agent-based simulations. Data are often anonymized or synthetic to protect privacy, and results are accompanied by sensitivity analyses and fairness assessments across user groups.

Applications and limitations: Platforms and research groups use testbanor to compare alternative policies, tune thresholds for automatic bans, and decide when human review should intervene. Limitations include reliance on representative data, potential biases in simulated environments, and ethical concerns about privacy and user harm.

See also: content moderation, policy testing, A/B testing, fairness in algorithms.