论文标题
在Twitter上进行意见预测的神经时间意见建模
Neural Temporal Opinion Modelling for Opinion Prediction on Twitter
论文作者
论文摘要
由于推文内容和邻里环境的短暂性质，Twitter上的意见预测是具有挑战性的。在本文中，我们将用户的推文发布行为建模为一个时间点过程，以共同预测鉴于用户的历史推文序列和邻居发布的Tweet，下一条推文的姿势标签。我们设计了一个主题驱动的注意机制，以捕获邻里环境中的动态主题变化。实验结果表明，与许多竞争基线相比，提出的模型可以更准确地预测未来推文的发布时间和立场标签。
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.
