Status: Ongoing
Associated with: Thynk360
Spoilers are a notorious problem on the internet, especially in fan communities discussing TV shows, movies, or games. To address this challenge, I am developing a Spoiler Blocker—an intelligent NLP-based web tool that automatically detects and hides spoiler content in user-generated posts. This project, developed under Thynk360, leverages state-of-the-art transformer models to identify spoiler-prone language patterns, aiming to preserve users’ content experience without heavy moderation.
Tools & Technologies Used
- Programming Language: Python
- Libraries & Frameworks: HuggingFace Transformers, Flask, Pandas, scikit-learn
- Model Type: Fine-tuned Transformer (BERT-based)
- Frontend: HTML/CSS with embedded Flask routes
- Deployment-ready components: RESTful API setup with Flask
Description
The idea was born from the need to provide spoiler-free browsing for fans in online forums, social platforms, and blog comment sections. The project began by curating a dataset of forum posts and Reddit threads labeled as either “spoiler” or “non-spoiler.” I cleaned and tokenized the data before feeding it into a BERT-based model via HuggingFace Transformers.
The model was fine-tuned for binary text classification and trained with attention to contextual cues, like mentions of key plot events, temporal references (“in the last episode”), and named characters in sensitive contexts.
Once trained, the model was embedded into a lightweight Flask web application, which allows users to paste text or integrate it into their web environment. Posts flagged as spoilers are blurred or hidden using CSS until the reader chooses to reveal them.
Key Highlights
- Fine-tuned a BERT model specifically for spoiler detection with over 92% accuracy.
- Developed a Flask-based web interface that scans and masks spoiler content in real-time.
- Built a custom vocabulary enrichment layer, giving the model context-specific sensitivity (e.g., recognizing spoiler names, locations, or events).
- Created a browser extension prototype using the same backend for direct deployment on discussion forums.
Learned / Achieved
This project significantly sharpened my skills in transfer learning, particularly in fine-tuning transformer models for domain-specific tasks. I learned how critical it is to customize pre-trained models using task-relevant data to improve real-world performance. The integration of Flask for serving the model was also a valuable experience, enabling me to bridge the gap between model training and application deployment.
On the frontend side, I gained experience in making AI solutions more user-friendly and visually intuitive, focusing on preserving user experience and trust. Designing logic to selectively blur content—without deleting or permanently modifying it—was both a technical and UX challenge.
Moreover, I learned how to build scalable pipelines for ingesting real-time content, predicting labels, and rendering outputs in milliseconds—skills highly relevant to AI product development.
Future Plans
I plan to enhance the Spoiler Blocker by incorporating multi-language support and user-based customization, such as sensitivity levels or content-specific keyword preferences. Another major goal is to integrate the backend into a browser extension for platforms like Reddit, Twitter, and Discord.
I’m also exploring ways to improve the system using Zero-Shot Classification and Named Entity Recognition (NER) to catch subtle, evolving spoiler patterns without retraining from scratch.
