Cyber Shield
Context-Aware Cyberbullying Detection on Social Media using NLP and Synthetic Data Generation.
Project Overview
Cyber Shield is an advanced Machine Learning implementation engineered to combat toxic comments and cyberbullying across digital interactions. Traditional filters rely strictly on dictionary blocklists and keyword matches, failing to interpret slang, syntactic subtleties, or contextual sarcasm. Cyber Shield remedies this by utilizing deep Transformer architectures to understand semantic sentence weights and detect nuances.
Core Capabilities
- Contextual Understanding: Uses transformer-based architectures to construct contextual word vectors, resolving semantic intent behind sentences.
- Synthetic Dataset Generation: Features custom synthetic augmentation procedures to construct additional minor-class toxic samples, restoring set balance.
- Inference Optimization: Programmed with clean pipeline hooks for processing, preprocessing, and classification tasks immediately.
Technology Stack
Challenges & Solutions
Extreme Class Imbalance
Standard online text datasets feature very small ratios of toxic speech compared to safe messages. Training a standard classifier directly leads the model to output safe labels universally.
Solution: Formulated an LLM-assisted Synthetic Data Generation engine. Augmented datasets dynamically to achieve balanced distributions, increasing validation Recall and F1 statistics.
Semantic Ambiguity
Harmless words are frequently combined in malicious orders, bypassing traditional keyword blockers that look at single token values out of order.
Solution: Implemented deep Transformers instead of Bag-of-Words metrics. The self-attention mechanisms compute weights between all word slots, flagging malicious setups successfully.