Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model

Shivashankar Subramanian, Timothy Baldwin, Trevor Cohn


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/P18-2030.pdf
Code
 shivashankarrs/Petitions