Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
This addresses opinion prediction on Twitter for social media analysis, but it is incremental as it builds on existing temporal modeling approaches.
The paper tackled the problem of predicting both the posting time and stance labels of future tweets by modeling user behavior as a temporal point process, achieving more accurate predictions compared to competitive baselines.
Opinion prediction on Twitter is challenging due to the transient nature of tweet content and neighbourhood context. In this paper, we model users' tweet posting behaviour as a temporal point process to jointly predict the posting time and the stance label of the next tweet given a user's historical tweet sequence and tweets posted by their neighbours. We design a topic-driven attention mechanism to capture the dynamic topic shifts in the neighbourhood context. Experimental results show that the proposed model predicts both the posting time and the stance labels of future tweets more accurately compared to a number of competitive baselines.