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

arXiv: https://arxiv.org/abs/1808.08538 · HTML: https://mlsanity.com/paper/1808.08538/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2018.

## Built on by (most cited first, 8 of 8)

- [How would Stance Detection Techniques Evolve after the Launch of ChatGPT?](https://mlsanity.com/paper/2212.14548/) (2022)
- [Adversarial Learning for Zero-Shot Stance Detection on Social Media](https://mlsanity.com/paper/2105.06603/) (2021)
- [Fine-Grained Prediction of Political Leaning on Social Media with Unsupervised Deep Learning](https://mlsanity.com/paper/2202.12382/) (2022)
- [A Survey of Stance Detection on Social Media: New Directions and Perspectives](https://mlsanity.com/paper/2409.15690/) (2024)
- [Modeling the Severity of Complaints in Social Media](https://mlsanity.com/paper/2103.12428/) (2021)
- [OpenStance: Real-world Zero-shot Stance Detection](https://mlsanity.com/paper/2210.14299/) (2022)
- [Improving Graph-Based Text Representations with Character and Word Level N-grams](https://mlsanity.com/paper/2210.05999/) (2022)
- [A Systematic Survey of Natural Language Processing for the Greek Language](https://mlsanity.com/paper/2407.09861/) (2024)

## Abstract (arXiv)

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.