[Meta] AI SPAM Filter

Goals & Guiding Principles

Provide an AI-powered spam filtering solution that integrates natively with Drupal's form systems to protect websites from unwanted content submissions. This functionality should leverage AI capabilities to intelligently detect and handle spam while maintaining user experience and editorial control.

Key principles:

• Utilize AI to enhance spam detection accuracy beyond traditional rule-based filters
• Build on existing Drupal patterns and integrate with Field Validation module architecture
• Support configurable scoring thresholds and automated response actions
• Provide transparent spam detection with explanations for editorial review
• Maintain flexibility to adapt to different scenarios and organizational spam policies
• Preserve legitimate user submissions while effectively blocking spam

Examples include AI-powered content analysis for webforms, automated comment moderation with confidence scoring, intelligent spam detection explanations, and configurable review workflows for flagged content.

Base functionality integrates with: Field Validation module →

MVP Functionality

Comment Form Protection Recipe

• Create a Drupal Recipe that protects Drupal comment forms using AI-based spam detection
• Implement a 5-point scoring system with automated actions:
  • Score 0-1: Automatically unpublish, no review required
  • Score 1-3: Unpublish and flag for human review
  • Score 3-5: Publish but flag for review
  • Score 5+: Publish without review required
• Define "Comment Reviewer" role with appropriate permissions
• Create intuitive comment review interface using Views module for efficient spam management

Webform Protection Recipe

• Develop generic Drupal Recipe for protecting webforms from spam submissions
• Implement automatic evaluation of all text fields for spam content detection
• Provide AI confidence scoring with human-readable explanations for flagged submissions
• Add spam detection results to dedicated webform fields for tracking and analysis
• Support configurable thresholds and actions based on spam confidence levels

Administrative Interface

Post MVP Functionality

• Add batch processing capabilities for reviewing multiple flagged submissions simultaneously
• Implement machine learning feedback loops to improve detection accuracy based on reviewer corrections
• Create advanced reporting and analytics for spam patterns and filter effectiveness
• Develop integration with external spam detection services for enhanced accuracy
• Add support for additional Drupal form types (contact forms, user registration, etc.)
• Implement whitelist/blacklist functionality for known good/bad patterns
• Create API endpoints for external systems to leverage spam detection capabilities

Out of scope

• Integration with third-party commercial spam services (initially - may be added post-MVP)
• Real-time learning from global spam databases
• Advanced natural language processing beyond core spam detection
• Integration with email spam filtering systems

More ideas

• Multi-language spam detection support
• Integration with Drupal's Ban module for repeat offenders
• Honeypot field integration for enhanced bot detection
• Rate limiting integration for suspicious IP addresses
• Content similarity detection to catch duplicate spam attempts
• Integration with CAPTCHAs as fallback protection
• Spam detection for media uploads and file attachments

Next Steps

• Review MVP functionality with stakeholders
• Collect additional ideas from community feedback
• Staff implementation team with Drupal and AI expertise
• Create detailed technical specifications
• Implement version 1 with comment and webform protection
• Establish testing protocols with real-world spam datasets