Macro is a conversational moderation framework designed to analyze the context of discussions rather than isolated messages in order to determine how a community moderation system should respond.

While traditional moderation tools classify individual messages as allowed or disallowed, real conversations are more complex. Language that appears aggressive in isolation may be completely harmless when used between friends, while seemingly neutral messages can become hostile depending on context.

Macro was built to address this gap by combining natural language understanding with conversational context analysis.

The system evaluates both the current message and recent conversation history to determine whether moderation action is needed, whether the conversation should be gently redirected, or whether no intervention is necessary.

Macro complements the AMAR moderation system.

AMAR focuses on semantic classification of individual messages, detecting spam, scams, and abusive content that traditional keyword filters miss.

Macro operates at a different layer by analyzing conversation flow and intent.

This layered design allows the system to first determine whether content is potentially harmful and then evaluate how the conversation should be managed.

Macro analyzes the current message alongside several recent messages in the conversation to determine intent.

This approach helps solve a common moderation problem:

• friendly conversations may contain profanity but no hostility
• discussions about sensitive topics may appear high-risk but remain civil
• conflicts may escalate gradually across multiple messages.

By observing the conversation as a whole, Macro can better determine whether a situation requires intervention.

Instead of treating moderation as a binary decision, Macro can apply different strategies depending on the situation.

Possible outcomes include:

If the system determines the conversation is normal or friendly, no action is taken.

If the conversation appears to be drifting toward conflict, Macro can introduce a friendly message designed to redirect tone and encourage constructive discussion.

This allows the system to de-escalate situations before they become harmful.

If the system detects a potentially hostile exchange that may require human judgment, the conversation can be flagged for moderator review.

If the message clearly violates moderation policies, the system can trigger automated removal actions.

Macro was trained using over 150,000 conversational messages derived from real-world sources including:

• developer community conversations
• submitted content samples
• reported moderation cases.

Using realistic conversational data helps the system better understand how people actually communicate online rather than relying solely on curated datasets.

Macro uses a lightweight transformer-based NLP model designed to balance accuracy with operational efficiency.

Unlike large language models that require significant compute resources, Macro's model architecture focuses on:

• fast inference
• predictable operating cost
• scalability for real-time moderation.

This allows the system to run continuously without the high infrastructure cost associated with large-scale LLM deployments.

One challenge encountered during development involved conversations about sensitive or high-risk topics.

In some cases, discussions that were technically harmless were initially flagged as risky because the subject matter appeared similar to hostile conversations.

To address this, ongoing improvements focus on helping the model better distinguish between:

• discussing sensitive topics
• expressing hostile intent.

Improving contextual understanding remains an important part of Macro's continued development.

Macro helps moderation systems move beyond simple rule enforcement toward conversation-aware community management.

By combining contextual analysis with flexible intervention strategies, the system can:

• reduce unnecessary moderation actions
• prevent conflicts from escalating
• support healthier community interactions.

Macro is publicly available through Discord's application directory and can be added to communities free of charge.

You can view the application here:

https://discord.com/discovery/applications/1300513139233656923

Providing the system publicly allows communities to experiment with context-aware moderation without requiring custom infrastructure or complex configuration.

Macro demonstrates how AI moderation systems can evolve beyond simple content classification into tools that understand the flow of conversations.

By analyzing context and intent, the system provides a more nuanced approach to moderation that supports both community safety and natural communication.