Judol Slayer.

Description

Judol Slayer is a moderation automation tool that helps clean YouTube comment sections of spam related to online gambling (“judol”). It automatically detects suspicious keywords, spam patterns, and unwanted promotional comments so creators can maintain healthier communities.

The tool emphasizes lightweight automation, fast comment processing, and a scalable moderation workflow that reduces manual moderation effort.

Background

Online gambling spam floods the comment sections of many YouTube channels, and removing it by hand is tedious and never-ending. Judol Slayer was built to automate that cleanup so channel owners can keep their comments usable without constant manual work.

Goal

• Automatically detect gambling/spam comments on YouTube.
• Remove flagged comments to reduce manual moderation.
• Keep filtering rules configurable and easy to extend.

Features

• Automatic spam comment detection and removal
• Keyword-based filtering and suspicious pattern detection
• Configurable filtering rules
• Lightweight, fast comment processing
• Scalable moderation workflow
• Easy customization support

Technologies

• Node.js — runtime for the automation and API interaction logic.
• Express.js — provides the server layer and endpoints for controlling the tool.

How to Run

npm install
# Configure .env with your YouTube Data API credentials/OAuth tokens
cp .env.example .env
npm start

Technical Decisions

• Built on Node.js to work naturally with the YouTube Data API and asynchronous request handling.
• Used Express.js to expose a simple server interface for triggering and controlling moderation runs.
• Kept detection rule/keyword-based so filters stay transparent and easy to tune without retraining a model.

What I Learned

• Working with the YouTube Data API for reading and moderating comments.
• Designing configurable keyword and pattern matching for spam detection.
• Structuring a lightweight automation service around Express.

Notes

Requires YouTube API access and appropriate permissions on the target channel. Keyword-based rules may need ongoing tuning as spam patterns evolve; future ideas include smarter pattern scoring and a review queue before deletion.