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Powered Hate Speech Detection

Our project tackled the pressing issue of mitigating hate speech within their online platforms in the aviation industry. This endeavor was crucial for enhancing user experience and safeguarding brand reputation against the adverse impacts of bullying, threats, and malicious content.

Project Name: Hate Speech Detection

Location: UK

Business Domain: Aviation Industry

Engagement Model: Managed Delivery (Solution Based)

Team Size: 1 ML Engineer

Key Technologies: BERT, BiLSTM, SQL, Python, Tableau, Snowflake, Amazon EKS


Operating in the aviation industry, the client faced the critical challenge of mitigating potential malicious or harmful content within their online community, app, or website. The presence of hate speech, bullying, and threats could significantly impact user experience and brand reputation.


To address this, we developed a sophisticated AI model utilizing BERT and BiLSTM algorithms, trained on the ETHOS Dataset specifically designed to recognize hate speech. This solution:

- Flagging and Action: Implemented algorithms to flag texts with high probability scores for further action, including automatic removal or human moderation.
- Adaptive Learning: The AI model learns and improves its accuracy over time, adapting to new patterns of speech and content.
- Customization: Tailored to align with specific legal requirements and platform guidelines.
- Deployment: Utilized a GPU-based Kubernetes cluster on Amazon EKS for robust and scalable deployment.
- Technological Integration: Combined SQL for database management, Python for programming, and Tableau for data visualization.

GPU-based Kubernetes cluster on Amazon EKS
Programming Languages: SQL, Python
Visualization: Tableau
Database: Snowflake


The implementation of our hate speech detection model has yielded significant improvements in several key performance indicators:

- Accuracy Improvement: Demonstrated remarkable accuracy in detecting hate speech across multiple languages, enhancing user communication and platform inclusivity.
- User Engagement Increase: Post-implementation data showed a substantial increase in user engagement, attributed to improved communication quality and reduced barriers.
- Reduction in Hate Speech Incidents: Successfully reduced the occurrences of hate speech, aligning with the client's values of respect and inclusivity.
- Enhanced User Experience: Users praised the seamless integration and intuitive nature of the model, contributing to increased platform loyalty and satisfaction.
- Scalability and Adaptability: The model efficiently handled the growing user base and language diversity without compromising performance.
- Customer Support Satisfaction: Ongoing developer support ensured the model's continuous optimization, receiving high satisfaction ratings.

In conclusion, the hate speech detection model has significantly elevated the platform's performance and user experience. The advancements in AI technology have not only improved the immediate platform environment but also set the stage for future technological enhancements and collaboration.

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