---
title: "Advanced Message Filtering for Platform Safety"
description: "A banned word list misses indirect insults and hints at meeting off-platform. We annotated messages in context, then validated every edge case."
url: "https://unidata.pro/cases/advanced-message-filtering-for-platform-safety/"
date_modified: "2026-05-18T17:45:14+03:00"
language: "en-US"
---
Client Request
--------------

Our client, a leading company in the classifieds industry, aimed to build a message filtering system that would:

- 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
------------

### 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)

### 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

### 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

| **Stage** | **Input** | **Workflow Scope** | **Main Quality Checks** |
|---|---|---|---|
| Project Setup | Client guidelines & chat data | Instruction review, clarification, tone alignment | Guideline clarity / linguistic consistency |
| Pilot Phase | Sample conversations | Testing annotation logic, resolving edge cases | Tone accuracy / ambiguity reduction |
| Annotation | Chat messages & reply suggestions | Labeling relevance, safety, tone, grammar | Context alignment / toxicity filtering |
| Linguistic Control | Annotated responses | Informal style, natural phrasing validation | Fluency / conversational realism |
| Validation & QA | Annotated batches | Sampling, validator review, escalation of edge cases | Accuracy / policy compliance |
| Feedback Loop | QA results | Performance tracking, annotator feedback | Error reduction / consistency |
| Training & Support | Validators | Ongoing training, targeted improvements | Validator accuracy |
| Final Delivery | Validated dataset | Packaging and handoff | Dataset readiness / deployment quality |

[View as Markdown](https://unidata.pro/cases/advanced-message-filtering-for-platform-safety.md)

## Hero

**Industry and use case:** E-commerce and Retail **Data:** Dialogues exchanged between users **Project duration:** 7 weeks

## Main title

Advanced Message Filtering for Platform Safety

## Description

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.

## Прогресс - результаты - цитата

### Прогресс - шаги

**Перечень шагов:**

- **Количество дней:** Project Setup & Guideline Alignment — **Описание шага:** 1 week
- **Количество дней:** Pilot Phase & Linguistic Calibration — **Описание шага:** 2 weeks
- **Количество дней:** Annotation & Validation Phase — **Описание шага:** 2 weeks
- **Количество дней:** Final Evaluation & Delivery — **Описание шага:** 1 week

### Результаты

**Перечень результатов:**

- 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

### Цитата

**Цитата:** In conversational AI, the hardest part isn’t detecting toxicity. It’s generating responses that are neutral, context-aware, and still sound human. That balance only comes from carefully annotated real dialogue.

**Автор:** Vladislav Barsukov

**Должность:** Head of SLM&LLM Annotation