Precision, recall, and F1 score are three linked measures of how well a moderation system finds violations. Precision is the share of flagged items that really break the rules. Recall is the share of real violations the system caught. F1 combines the two into one number using their harmonic mean.

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‍Why it matters

Moderation has two ways to fail. Flagging harmless content is a false positive, which hurts precision. Missing real violations is a false negative, which hurts recall. Which one matters more depends on the harm. For the most severe categories, teams lean toward recall and accept extra false alarms, since a miss is costlier. For low-severity categories, precision usually gets more weight so users aren't penalized wrongly.

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‍How to read them together

Report all three per policy category, not as one blended figure, since a system can be strong on spam and weak on harassment. Watch what happens when the flagging threshold moves. Lowering it usually raises recall and lowers precision, and the reverse when it's raised.

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‍A worked example

Say a system reviews 1,000 posts, and 100 of them really violate policy. It flags 120 posts, and 80 of those are real violations. Precision is 80 out of 120, about 67%. Recall is 80 out of 100, or 80%. F1 comes out around 73%. Twenty real violations were missed and 40 harmless posts were flagged, and the single F1 number hides which of those is the bigger problem.