Content-based Popularity Prediction of Online Petitions Using a Deep Regression Model
This work addresses a domain-specific problem for policy-makers and petition posters, but it is incremental as it applies a hybrid deep learning method to existing data.
The authors tackled the problem of predicting online petition popularity from textual content using a CNN regression model with an auxiliary ordinal objective, achieving effectiveness on UK and US government datasets.
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.