Abstract
Online petitions are a cost-effective way for citizens to collectively engage with policy-makers in a democracy. Predicting the popularity of a petition — commonly measured by its signature count — based on its textual content has utility for policymakers as well as those posting the petition. In this work, we model this task using CNN regression with an auxiliary ordinal regression objective. We demonstrate the effectiveness of our proposed approach using UK and US government petition datasets.- Anthology ID:
- P18-2030
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 182–188
- Language:
- URL:
- https://aclanthology.org/P18-2030
- DOI:
- 10.18653/v1/P18-2030
- Cite (ACL):
- Shivashankar Subramanian, Timothy Baldwin, and Trevor Cohn. 2018. Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 182–188, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model (Subramanian et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/P18-2030.pdf
- Code
- shivashankarrs/Petitions