coflags

Coflags are a conceptual framework used to represent the aggregated outcome of multiple flagging signals about a piece of content or user behavior. The term combines co- (together) with flags (indicators of potential policy violations) to denote a consolidated status that reflects contributions from diverse sources, such as user reports, automated detectors, and human moderators.

Data model and terminology. Each coflag includes a set of dimension flags (for example, violence, harassment, spam), a status (clear, flagged, escalated, resolved), a confidence score, and a provenance log. The confidence score expresses the overall certainty derived from the contributing signals, while the provenance records which sources contributed which flags and when. This structure supports auditing and traceability in moderation workflows.

Types and aggregation. Coflags come in several forms: simple coflags (binary indicators across dimensions), composite coflags (multiple dimensions grouped into a single category), and weighted or cohort coflags (sources have relative influence). Aggregation rules vary by system, with common approaches including majority vote, weighted sums, and threshold-based escalation to human review.

Workflow and applications. In practice, new signals trigger the computation of coflags, which in turn determine moderation actions such as queuing for review, automatic removal, or escalation to human moderators. The history of coflags supports post-hoc analysis, policy refinement, and cross-platform auditing. Applications include large-scale content moderation, research into governance models, and studies of collaborative decision-making in online communities.

Limitations and governance. Challenges include potential bias in sources, signal drift, privacy concerns, latency, and interpretability of the aggregated status. Effective use requires transparent rules, regular audits, and safeguards to balance responsiveness with fairness.

See also: flagging, content moderation, crowdsourcing.