Neural Temporal Opinion Modelling for Opinion Prediction on Twitter

TL;DR

This paper introduces a neural temporal opinion model for Twitter that predicts when and what stance a user will take in their next tweet by modeling their posting behavior and neighborhood context.

Contribution

It proposes a novel temporal point process model with topic-driven attention to jointly predict tweet timing and stance, improving over existing baselines.

Findings

Enhanced prediction accuracy for tweet timing and stance.

Effective modeling of dynamic topic shifts in neighborhood context.

Outperforms competitive baseline models.

Abstract

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.