HeReFaNMi is a project aimed at detecting and mitigating health-related misinformation using natural language processing. Medical fake news is a particularly high-stakes domain (bad information about treatments, vaccines, or symptoms can cause real harm), so this repository gathers tooling to help identify and push back against that kind of content.

At its core the project frames misinformation mitigation as an NLP problem: classifying and flagging health-related claims so that dubious content can be surfaced rather than left to spread unchecked. It is a focused effort on a domain where accuracy and caution matter more than usual, and where the cost of a wrong prediction is not just an error metric.

This connects to my broader work in NLP and applied machine learning. Building classifiers for a sensitive, real-world domain is a useful reminder that model behavior has to be evaluated with the downstream consequences in mind, not only benchmark numbers.

Explore the code on GitHub.