Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum

Tsakalidis, Adam; Aletras, Nikolaos; Cristea, A.I.; Liakata, Maria

Abstract

Modelling user voting intention in social media is an important research area, with applications in analysing electorate behaviour, online political campaigning and advertising. Previous approaches mainly focus on predicting national general elections, which are regularly scheduled and where data of past results and opinion polls are available. However, there is no evidence of how such models would perform during a sudden vote under time-constrained circumstances. That poses a more challenging task compared to traditional elections, due to its spontaneous nature. In this paper, we focus on the 2015 Greek bailout referendum, aiming to nowcast on a daily basis the voting intention of 2,197 Twitter users. We propose a semi-supervised multiple convolution kernel learning approach, leveraging temporally sensitive text and network information. Our evaluation under a real-time simulation framework demonstrates the effectiveness and robustness of our approach against competitive baselines, achieving a significant 20% increase in F-score compared to solely text-based models.

Citation

Tsakalidis, A., Aletras, N., Cristea, A., & Liakata, M. (2018, December). Nowcasting the Stance of Social Media Users in a Sudden Vote: The Case of the Greek Referendum. Presented at 2018 ACM Conference on Information and Knowledge Management (CIKM’18), Torino

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2018 ACM Conference on Information and Knowledge Management (CIKM’18)
• Online Publication Date: Oct 17, 2018
• Publication Date: Oct 1, 2018
• Deposit Date: Aug 28, 2018
• Publicly Available Date: Aug 28, 2018
• Publisher: Association for Computing Machinery (ACM)
• Pages: 367-376
• Book Title: Proceedings of the 27th ACM International Conference on Information and Knowledge Management.
• DOI: https://doi.org/10.1145/3269206.3271783
• Public URL: https://durham-repository.worktribe.com/output/1145813