Message Filtering and Classification for a Classifieds Platform

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When user trust is at stake, platforms can't afford to let harmful messages slip through.

A major classifieds company needed a reliable way to protect conversations on their platform — without slowing them down. To make that happen, Unidata provided high-precision annotation and validation for a smart filtering and classification system that now helps keep millions of daily interactions safe and respectful.

Industry E-commerce and Retail
Timeline 3 months
Data Dialogues exchanged between users
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Industry E-commerce and Retail
Timeline 3 months
Data Dialogues exchanged between users

Client Request

The client—a major player in the classifieds space—sought to develop a message filtering system that could:

  • Prevent the spread of inappropriate or restricted content
  • Improve overall conversation quality on the platform
  • Protect users from violations such as:
    • Offensive or abusive language
    • Personal data disclosure
    • Negative or harmful speech

To achieve this, Unidata was brought in to annotate and validate the dataset, providing the foundation for a model that could reliably detect and categorize sensitive content.

Our Approach

  • 01

    Technical Requirements and Pilot Phase

    The client provided a detailed technical brief outlining classification requirements. Our team proposed additional refinements to ensure a more precise and layered annotation process.

    During the pilot phase, we collaborated closely with the client to:

    • Clarify classification rules for key categories, including:
      • Insults and abusive language
      • Mentions of personal information
      • References to meeting arrangements
      • Negative sentiment directed at the platform
    • Address complex edge cases, such as:
      • Implicit mentions of meeting locations (e.g., vague geographic references without full addresses)
  • 02

    Annotation and Quality Control Process

    Our annotation team at Unidata handled classification by carefully considering:

    • Platform-specific communication patterns
    • Informal language use typical in peer-to-peer messaging
    • The context of each message, not just isolated phrases

    Messages were annotated across several primary categories:

    • Use of profanities or slurs
    • Disclosure of personal or sensitive information
    • Various forms of direct and indirect insults
    • Mentions of meeting points or negotiation outside the platform
  • 03

    Data Validation

    To ensure the highest level of annotation accuracy, we implemented a robust validation workflow:

    • Involved experienced validators to review annotated samples
    • Introduced an interactive error analysis process, which included:
      • Team discussions of edge cases
      • Targeted surveys to refine judgment on difficult categories

    We also conducted training and testing sessions with annotators focused on:

    • Eliminating errors in high-complexity cases
    • Aligning the team on annotation logic and edge-case handling
    • Ensuring consistent interpretation of classification criteria

Results

  • The model trained on our annotated data was successfully tested and deployed on the client’s platform. Internal testing involved evaluating model performance against randomly selected user messages

  • The initial testing phase showed promising results:

    • The model accurately blocked inappropriate or restricted content
    • Responses remained contextually appropriate across various scenarios

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