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Modelling user voting intention in social media is an important researcharea, with applications in analysing electorate behaviour, online politicalcampaigning and advertising. Previous approaches mainly focus on predictingnational general elections, which are regularly scheduled and where data ofpast results and opinion polls are available. However, there is no evidence ofhow such models would perform during a sudden vote under time-constrainedcircumstances. That poses a more challenging task compared to traditionalelections, due to its spontaneous nature. In this paper, we focus on the 2015Greek bailout referendum, aiming to nowcast on a daily basis the votingintention of 2,197 Twitter users. We propose a semi-supervised multipleconvolution kernel learning approach, leveraging temporally sensitive text andnetwork information. Our evaluation under a real-time simulation frameworkdemonstrates the effectiveness and robustness of our approach againstcompetitive baselines, achieving a significant 20% increase in F-score comparedto solely text-based models.

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