Tsakalidis, Adam and Aletras, Nikolaos and Cristea, A.I. and Liakata, Maria (2018) 'Nowcasting the stance of social media users in a sudden vote : the case of the Greek referendum.', in Proceedings of the 27th ACM International Conference on Information and Knowledge Management. New York, NY, USA: Association for Computing Machinery, pp. 367-376.
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.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1145/3269206.3271783|
|Publisher statement:||© 2018 Copyright held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 27th ACM International Conference on Information and Knowledge Management, https://doi.org/10.1145/3269206.3271783|
|Date accepted:||No date available|
|Date deposited:||28 August 2018|
|Date of first online publication:||17 October 2018|
|Date first made open access:||No date available|
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