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

TL;DR

This paper presents a deep learning model that predicts petition popularity from text content, aiding policymakers and petitioners by estimating signature counts based on petition descriptions.

Contribution

It introduces a CNN regression model with an auxiliary ordinal regression task for improved accuracy in popularity prediction from petition texts.

Findings

Effective prediction of petition signatures using CNN regression.

Model outperforms baseline methods on UK and US datasets.

Auxiliary ordinal regression enhances prediction accuracy.

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 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.