A HYBRID BERT–GNN FOR DETECTING HOAXES AND NEGATIVE CONTENT IN INDONESIAN SOCIAL MEDIA - Khairunnisa; Khairunnas; Sutriawan (LPPM Nusa Mandiri, 2026)
The rapid spread of hoaxes on social media threatens public trust and information integrity, especially within the Indonesian digital landscape. This study proposes a hybrid deep learning model that integrates transformer-based semantic representation from IndoBERT with Graph Neural Networks (GNNs) to enhance hoax detection performance. A heterogeneous social graph is constructed to model relationships among posts, users, and news sources, where post node features are extracted from the [CLS] embeddings of a fine-tuned IndoBERT. The GNN component consists of two graph convolutional layers with ReLU activation and dropout, followed by a multilayer perceptron classifier for binary classification. Experiments conducted on the Indonesia False News dataset (Kaggle) employ SMOTE resampling to handle class imbalance and 5-fold stratified cross-validation for robust evaluation across three configurations: BERT-only, GNN-only, and the proposed BERT–GNN hybrid model. The hybrid model achieves an average F1-score of 0.89 ± 0.01 and ROC-AUC of 0.92 ± 0.01, outperforming both single-model baselines while maintaining a balanced precision–recall trade-off. These results confirm that combining contextual semantic understanding with relational graph topology substantially enhances accuracy, robustness, and generalization in detecting hoaxes within Indonesian-language social media content