Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
Table 1: Stance prediction accuracy and Mean Squared Errors of predicted posting time on the Brexit and Election datasets.
['Model', 'Brexit Acc.', 'Brexit MSE', 'Election Acc.', 'Election MSE']
Compared to baselines, NTOM consistently achieves better performance on both datasets, showing the benefit of modelling the tweet posting sequence as a temporal point process. In the second set of experiments, we study the effect of temporal process modelling. The results verify the benefit of using the intensity function, with at least a 2% increase in accuracy and 0.2 decrease in MSE compared with vanilla RNN and its variants. In the ablation study, the removal of neighbourhood context component caused the largest performance decline compared to other components, verifying the importance of social influence in opinion prediction. Removing either VAE (for topic extraction) or intensity function (using only GRU) results in slight drops in stance prediction and more noticeable performance gaps in time prediction. It can be also observed that using GCN to model higher-order influence in social networks does not bring any benefits, possibly due to extra noise introduced to the model.