Enterprise AI Analysis
WATCHED: A Web AI Agent Tool for Combating Hate Speech by Expanding Data
Online harms are a growing problem in digital spaces, putting user safety at risk and reducing trust in social media platforms. One of the most persistent forms of harm is hate speech. To address this, we need tools that combine the speed and scale of automated systems with the judgement and insight of human moderators. These tools should not only find harmful content but also explain their decisions clearly, helping to build trust and understanding. In this paper, we present WATCHED a chatbot designed to support content moderators in tackling hate speech. The chatbot is built as an Artificial Intelligence Agent system that uses Large Language Models along with several specialised tools. It compares new posts with real examples of hate speech and neutral content, uses a BERT-based classifier to help flag harmful messages, looks up slang and informal language using sources like Urban Dictionary, generates chain-of-thought reasoning, and checks platform guidelines to explain and support its decisions. This combination allows the chatbot not only to detect hate speech but to explain why content is considered harmful, grounded in both precedent and policy. Experimental results show that our proposed method surpasses existing state-of-the-art methods, reaching a macro F1 score of 0.91. Designed for moderators, safety teams, and researchers, the tool helps reduce online harms by supporting collaboration between AI and human oversight.
Executive Impact & Key Performance Indicators
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Overall F1 Score with WATCHED
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Hate Speech Classifier Usage
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Achieved Macro F1 Score
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Achieved Micro F1 Score
Deep Analysis & Enterprise Applications
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Motivation and Significance
Hate Speech remains a serious social media issue, threatening individual safety and community cohesion. Platforms often prioritize engagement over consistent enforcement, leaving human moderators overwhelmed. Automated tools are crucial for rapid, large-scale content analysis, but current systems struggle with implicit language, slang, sarcasm, and context, often lacking interpretability. This highlights the urgent need for advanced AI solutions like WATCHED.
Software Description
WATCHED is a chatbot designed as an AI Agent system utilizing Large Language Models (LLMs) and specialized tools. It features a front-end for user input and feedback, and a back-end orchestrating the AI agent to process inputs, retrieve context, reason, and generate responses. Unlike fixed pipelines, the agent adapts and synthesizes evidence from various sources like a BERT-based classifier, Urban Dictionary, similar post databases, and a multi-step reasoning LLM.
System Evaluation
The system was evaluated using the MetaHate dataset, a collection of over 1.2 million English hate speech posts. A reannotated subsample of 2001 instances, distinct from the RAG database, was used for evaluation. WATCHED was compared against baselines including MetaHateBERT, Distil MetaHate, Llama 3 8B, and Perspective API, demonstrating superior performance across all F1 metrics. An ablation study further confirmed the crucial role of each integrated tool.
Impact & Conclusion
WATCHED offers a significant advancement in combating online hate speech by providing an interpretable and adaptable AI Agent system. Its ability to detect, explain, and augment hate speech data, combined with human-in-the-loop validation, addresses limitations of prior systems. The tool supports social media moderators, safety teams, and researchers, fostering better human-AI collaboration and enabling continuous adaptation to evolving hateful discourse and linguistic trends.
0.91
Overall F1 Score with WATCHED
Enterprise Process Flow
User Query
→
AI Agent Orchestration
→
External Tool Integration
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Reasoned Judgement
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Platform Guideline Alignment
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Human Feedback Loop
| Model | Macro F1 Score | Key Features |
|---|---|---|
| WATCHED | 0.9139 |
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| Distil MetaHate | 0.8807 |
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| MetaHateBERT | 0.8801 |
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| Llama 3 70b (Few-Shot CoT) | 0.8496 |
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Real-world Hate Speech Detection & Explanation
A user inputs 'all immigrants are a burden to our society!!! white power'. WATCHED classifies it as Hate Speech: True with 0.99 confidence. It explains the harmful narrative towards immigrants, linking 'white power' to racial superiority and discrimination. The system then grounds this in UN guidelines on prohibiting advocacy of national, racial, or religious hatred. This process demonstrates WATCHED's ability to not only detect but also justify its decisions by citing relevant policy.
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Estimated Annual Savings
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Hours Reclaimed Annually
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Implementation Roadmap
A phased approach to integrating WATCHED into your existing moderation workflows, ensuring seamless transition and maximum impact.
Phase 1: Discovery & Customization (2-4 Weeks)
Initial consultation to understand your specific content moderation challenges and platform guidelines. Customization of WATCHED's tools and LLM agents to align with your unique requirements and integrate with existing systems.
Phase 2: Pilot Program & Data Integration (4-8 Weeks)
Deployment of WATCHED in a controlled environment with a subset of your moderation team. Integration with your content database and initial fine-tuning based on real-world data and human feedback.
Phase 3: Full Deployment & Training (3-6 Weeks)
Rollout of WATCHED across your entire moderation operation. Comprehensive training for your teams on leveraging the AI agent for enhanced detection, explanation, and data augmentation.
Phase 4: Optimization & Continuous Learning (Ongoing)
Ongoing monitoring, performance review, and iterative improvements. Utilizing the human-in-the-loop feedback mechanism to ensure WATCHED continuously adapts to evolving language patterns and policy changes, maintaining state-of-the-art effectiveness.
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