Rumour Detection via News Propagation Dynamics and User Representation Learning
TABLE II: Extended rumour detection performance of the DRRD model in comparison with baseline models (R:Rumour, N:Non-Rumour)
['Model', 'Class', 'Weibo Accuracy', 'Weibo Precision', 'Weibo Recall', 'Weibo  [ITALIC] F1', 'Twitter Accuracy', 'Twitter Precision', 'Twitter Recall', 'Twitter  [ITALIC] F1']
The CAMI and CSI models, which are deep-learning-based models, achieve good performance; nevertheless, the proposed model delivers the best performance for both datasets. Specifically, our model yields the best detection results in terms of accuracy, precision, recall and F1-score on the Weibo dataset. On the Twitter dataset, our model achieves comparable results with other models in terms of the precision and recall metrics, and the best results in terms of the accuracy and F1-score metrics. Specifically, using the proposed user module improves the accuracy by more than 2% compared to the SVD-based counterpart on both the Weibo and Twitter datasets. It also leads to better performance in terms of the precision, recall and F1-score metrics on both datasets.