You must design and evaluate a bot-detection system for comment activity. Address: 1) Labeling strategy with minimal ground truth: propose weak-supervision heuristics, manual review sampling plans, and how you’d de-bias labels given extreme class imbalance (e.g., <0.5% bots). 2) Features across time scales: per-session burstiness, inter-comment intervals, entropy of targets, language signals, graph-based reciprocity; specify which must be real-time vs. batch. 3) Model choice and calibration: compare linear, tree ensembles, and sequence models; how to calibrate posteriors (Platt/Isotonic) and monitor calibration drift. 4) Thresholding by cost: define costs for FP (blocking a human) vs FN (missing a bot) and pick an operating point using precision-recall curves; compute expected blocked-human-minutes at a chosen threshold given example rates. 5) Adversarial robustness: features least gameable, canaries, and drift detection. 6) Online safety net: shadow mode, backfill re-scoring, human review queues. 7) Evaluation: offline PR-AUC/recall@high-precision; online guardrails (reports, creator retention, comment latency). 8) If FP becomes high, trace the root cause with error analysis and propose a rollback/ramp strategy.