Modeling Conversation Structure and Temporal Dynamics for Jointly Predicting Rumor Stance and Veracity
Table 4: Ablation tests of stance features and temporal modeling for veracity prediction on PHEME dataset.
['[BOLD] Method', 'Macro- [ITALIC] F1', 'Acc.']
5.4.2 Ablation Tests Effect of Stance Features To understand the importance of stance features for veracity prediction, we conduct an ablation study: we only input the content features of all tweets in a conversation to the top component RNN. It means that the RNN only models the temporal variation of tweet contents during spreading, but does not consider their stances and is not “stance-aware”. Hence, combining the low-level content features and the high-level stance features is crucial to improve rumor veracity prediction. Effect of Temporal Evolution Modeling We modify the Stance-Aware RNN by two ways: (i) we replace the GRU layer by a CNN that only captures local temporal information; (ii) we remove the GRU layer.