Stop Discord spam without blocking real members

Repeated text, mention floods, invite links, suspicious domains and rapid channel hopping are different problems. Give each pattern its own threshold instead of applying one aggressive message limit everywhere.

Observe normal activity during both quiet and busy periods. A gaming event can produce bursts that look abnormal on an average day. Base limits on the channel’s purpose and update them after real false positives.

Message speed alone is rarely enough to justify a ban. Combine it with repeated content, account age, join time, link reputation and the number of affected channels. Several weak signals together are more reliable than one rigid rule.

Use slower modes and temporary message deletion for uncertain cases. Reserve automatic timeouts or bans for high-confidence patterns such as identical scam links posted across multiple channels.

New-member channels, direct-message campaigns and public invite links deserve stricter protection. Add a brief verification or membership delay before new accounts can post links or mention many people.

Do not make verification so difficult that legitimate users leave. Explain each step, offer a support path and test the flow from a new account on mobile as well as desktop.

Review blocked events every week at first. Record how many were true attacks, harmless bursts and uncertain cases. Lower a threshold only when the logs show a recurring gap; raise it when normal members are repeatedly caught.

A good anti-spam system is quiet. Members rarely notice it, moderators receive useful context and attackers cannot learn a single predictable threshold to evade.

Before choosing thresholds, collect a normal week of activity for each channel type. Record the busiest legitimate minute, the number of repeated messages, how often links appear and which roles create automated posts. A support channel, live event channel and general chat should not inherit the same limits simply because they belong to the same server.

Build a small test matrix from that baseline. A member sending five different answers during a live quiz is not the same as a new account sending the same link five times across unrelated channels. Define which combination of speed, duplication, account age and channel spread moves an event from observation to deletion, timeout or staff review.

Roll out one signal at a time in log-only mode when possible. Read the alerts, label false positives and adjust the threshold before enabling punishment. This produces evidence for the setting and prevents a single configuration change from silencing normal members during the busiest period of the week.

Every automated action needs a recovery path. Log the triggering messages, rule identifier, action and expiry time in a channel visible to the moderation team. A moderator should be able to reverse a timeout, restore an incorrectly removed message when appropriate and explain the decision without guessing which rule fired.

Track false positives by rule rather than as one global number. If one link filter creates most reversals, narrow that filter instead of weakening the entire anti-spam system. Keep a short allow-list for domains the community genuinely uses, but require an owner and review date so the list does not become a permanent bypass for forgotten services.

After a real raid, compare what the automation caught with what moderators handled manually. Add a new signal only when it describes a repeatable pattern. Copying the attacker’s exact text into a permanent filter usually creates brittle protection; identifying the behavior—rapid duplication, account clustering or cross-channel spread—generalizes better.