Enhancing Fake News Detection with Explainable Natural Language Processing

A fake news detector made more trustworthy with explainable natural language processing.

A fake news detector made more trustworthy with explainable natural language processing. As a reference project it shows how a system like this is put together in practice, which parts matter and how they are tested, so that a developer can learn from it and build on it.

It suits learners who want to work with language models and text or speech: preparing text data, using or fine-tuning a model, evaluating it and building an interface around it.

The main parts are explanations of each result (feature importance or visual evidence), text or speech data preparation, model training, fine-tuning or prompting, evaluation with task-appropriate metrics, and a chat or web interface to use it. The reference build uses SHAP, Python, Hugging Face Transformers, spaCy, and PyTorch, with a clear project structure, setup instructions and sample data so that it can be run and extended.