Hybrid Multimodal Graph Neural Network for Explainable Fake News Detection on Social Media

A hybrid multimodal graph neural network that detects fake news on social media and explains why a post was flagged.

A hybrid multimodal graph neural network that detects fake news on social media and explains why a post was flagged. 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), fusion of more than one type of input, text or speech data preparation, model training, fine-tuning or prompting, and evaluation with task-appropriate metrics. The reference build uses PyTorch Geometric, SHAP, Python, Hugging Face Transformers, and spaCy, with a clear project structure, setup instructions and sample data so that it can be run and extended.