We then transform the sequence to a vector v by a max-pooling function that captures the global information of stance evolution, and feed it into a one-layer feed-forward neural network (FNN) with softmax normalization to produce the predicted veracity distributionv: The loss function of C for veracity prediction is also computed by cross-entropy criterion: where v denotes the veracity label of C. To leverage the interrelation between the preceding task (stance classification) and the subsequent task (veracity prediction), we jointly train two components in our framework.
we add two tasks' loss functions to obtain a joint loss function L (with a trade-off parameter λ), and optimize L to train our framework: In our Hierarchical-PSV, the bottom component Conversational-GCN learns content and stance features, and the top component Stance-Aware RNN takes the learned features as input to further exploit temporal evolution for predicting rumor veracity.