ThesisResearchAI/ML 8-12 months
Graph Neural Networks for Social Network Analysis and Fake News Detection
Using Graph Neural Networks (GNNs) to detect fake news by modeling the propagation structure on social networks capturing both article content and the way real vs fake news spreads through networks.
PythonPyTorch GeometricNetworkXHugging Face TransformersNeo4jFastAPI
Find GitHub repos & source code →How to build it step by step
- 1Dataset Construction: Use FakeNewsNet / LIAR datasets; build propagation graphs: news article → retweet cascade → user network
- 2GNN Architecture: Implement GraphSAGE or GAT; node features = BERT embeddings of tweet/post text + user credibility scores
- 3Heterogeneous Graph: Model multiple node types (News, User, Tweet); use HAN (Heterogeneous Attention Network) for multi-type graphs
- 4Evaluation: Compare to text-only BERT baseline; ablation study on graph topology vs content contribution
Key features to implement
- ✓Graph-based fake news classification
- ✓Propagation pattern visualization
- ✓User credibility scoring from historical behavior
- ✓Real-time detection API
- ✓Explainability: which subgraph triggered the prediction
💡 Unique twist to stand out
Investigate temporal graph learning how does the spread pattern at T=1h vs T=24h differ for fake vs real news? Use dynamic graph models to catch fake news in its early propagation phase.
🎓 What you'll learn
Graph neural networks, social network analysis, NLP + graph fusion models, misinformation detection, and research paper writing.