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

arXiv: https://arxiv.org/abs/1805.06566 · HTML: https://mlsanity.com/paper/1805.06566/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 May 2018.

## Built on by (most cited first, 1 of 1)

- [Towards Proactively Forecasting Sentence-Specific Information Popularity within Online News Documents](https://mlsanity.com/paper/2301.00152/) (2022)

## Abstract (arXiv)

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 policy-makers 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.