Neural Temporal Opinion Modelling for Opinion Prediction on Twitter

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3804–3810 July 5 - 10, 2020. c⃝2020 Association for Computational Linguistics 3804 Neural Temporal Opinion Modelling for Opinion Prediction on Twitter Lixing Zhu† Yulan He†∗ Deyu Zhou§ †Department of Computer Science, University of Warwick, UK §School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, China {Lixing.Zhu,Yulan.He}@warwick.ac.uk d.zhou@seu.edu.cn 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 tem- poral point process to jointly predict the post- ing 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 ac- curately compared to a number of competitive baselines. 1 Introduction Social media platforms allow users to express their opinions online towards various subject matters. Despite much progress in sentiment analysis in so- cial media, the prediction of opinions, however, remains challenging. Opinion formation is a com- plex process. An individual’s opinion could be influenced by their own prior belief, their social circles and external factors. Existing studies often assume that socially connected users hold similar opinions. Social network information is integrated with user representations via weighted links and encoded using neural networks with attentions or more recently Graphical Convolutional Networks (GCNs) (Chen et al., 2016; Li and Goldwasser, 2019). This strand of work, including (Chen et al., 2018; Zhu et al., 2020; Del Tredici et al., 2019), leverages both the chronological tweet sequence and social networks to predict users’ opinions. The majority of previous work requires a man- ual segmentation of a tweet sequence into equally- spaced intervals based on either tweet counts or ∗Corresponding author time duration. Models trained on the current inter- val are used to predict users’ opinions in the next interval. However, we argue that such a manual segmentation may not be appropriate since users post tweets at different frequency. Also, the time in- terval between two consecutively published tweets by a user is important to study the underlying opin- ion dynamics system and hence should be treated as a random variable. Inspired by the multivariate Hawkes process (Aalen et al., 2008; Du et al., 2016), we propose to model a user’s posting behaviour by a temporal point process that when user u posts a tweet d at time t, they need to decide on whether they want to post a new topic/opinion, or post a topic/opinion influenced by past tweets either posted by other users or by themselves. We thus propose a neu- ral temporal opinion model to jointly predict the time when the new post will be published and its associated stance. Instead of using the fixed …

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