Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
Table 3: Results of veracity prediction. Single-task setting means that stance labels cannot be used to train models.
['[BOLD] Setting', '[BOLD] Method', '[BOLD] SemEval dataset Macro- [ITALIC] F1', '[BOLD] SemEval dataset Acc.', '[BOLD] PHEME dataset Macro- [ITALIC] F1', '[BOLD] PHEME dataset Acc.']
By comparing single-task methods, Hierarchical GCN-RNN performs better than TD-RvNN, which indicates that our hierarchical framework can effectively model conversation structures to learn high-quality tweet representations. The recursive operation in TD-RvNN is performed in a fixed direction and runs over all tweets, thus may not obtain enough useful information. Moreover, the training speed of Hierarchical GCN-RNN is significantly faster than TD-RvNN: in the condition of batch-wise optimization for training one step over a batch containing 32 conversations, our method takes only 0.18 seconds, while TD-RvNN takes 5.02 seconds.