NLP Misinformation & Named Entity Extraction Engine

This NLP engine was built to ingest high-velocity, high-noise social data streams, extract vital named entities, and heuristically flag potential misinformation campaigns.

The sheer volume of unstructured text generated on social platforms makes manual moderation impossible. Identifying coordinated disinformation requires sophisticated tokenization and entity extraction to map out relationships and identify suspicious patterns.

I developed an end-to-end natural language processing pipeline in Python. By leveraging statistical embeddings, the system transforms unstructured text into structured, queryable data, automatically identifying people, organizations, and geographic locations (NER).

Statistical embeddings — Utilized advanced vector embeddings to capture semantic relationships between words, allowing the engine to detect subtle nuances in disinformation narratives.

Optimized tokenization — Implemented custom tokenization primitives specifically tuned for the chaotic, slang-heavy nature of social media text streams.

The model effectively isolated critical entities from highly unstructured data and successfully highlighted statistically anomalous text patterns indicative of coordinated misinformation.