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FakeNewsDetection
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AuthenticNews
Inspiration
The rise of misinformation and fake news has become a global challenge, impacting societies, democracies, and individual lives. Inspired by the need for trustworthy information and the power of open-source AI, we set out to build TruthCheck—a transparent, reproducible, and community-driven solution for fake news detection.
What We Learned
- Natural Language Processing (NLP): We deepened our understanding of transformer models, especially BERT, and how transfer learning can be leveraged for text classification tasks.
- Open Source Best Practices: We learned the importance of clear documentation, reproducibility, and proper crediting of datasets and code.
- Deployment: We gained hands-on experience deploying machine learning models as interactive web apps using Streamlit and Hugging Face Spaces.
- Collaboration: Working as a team, we improved our skills in version control, code review, and collaborative problem-solving.
How We Built It
- Data: We combined two open datasets: the Kaggle Fake and Real News Dataset and the LIAR dataset, preprocessing and merging them for robust training.
- Model: We used the pre-trained BERT-base-uncased model as our foundation, adding a BiLSTM and attention layer for enhanced context and interpretability. All layers were fine-tuned on our dataset.
- Training: The model was trained using PyTorch, with careful attention to validation, early stopping, and metric tracking (accuracy, precision, recall, F1).
- Web App: We built an interactive Streamlit app, allowing users to paste news articles and instantly receive a fake/real prediction, with a clean and user-friendly UI.
- Open Source: All code, training scripts, and deployment instructions are open and available in our GitHub repository.
Challenges We Faced
- Data Quality & Balance: Merging datasets with different formats and label distributions required careful preprocessing and validation.
- Model Size & Deployment: Handling large model files and ensuring smooth deployment on resource-limited platforms like Streamlit Cloud and Hugging Face Spaces.
- Dependency Management: Ensuring all dependencies were compatible and could be installed in cloud environments without compilation errors.
- Reproducibility: Making sure every step—from data download to model inference—could be reproduced by anyone, anywhere.
Math & Model
We leveraged the power of transfer learning, where a pre-trained model ( f_{\text{BERT}}(x; \theta) ) is fine-tuned on our dataset:
[ \text{Prediction} = \text{Softmax}(\text{Attention}(\text{BiLSTM}(f_{\text{BERT}}(x; \theta)))) ]
where ( x ) is the input text and ( \theta ) are the BERT parameters, all of which are updated during training.
TruthCheck is our contribution to the fight against misinformation—open, transparent, and built for the community.
