Party Prediction for Twitter

TL;DR

This paper surveys and empirically compares various methods for predicting political party affiliation on Twitter, introducing new approaches that are efficient and outperform existing models, with extensive experiments on data and transfer capabilities.

Contribution

It provides a comprehensive comparison of party prediction models, introduces new competitive approaches requiring less resources, and offers insights into data types and transferability for social media analysis.

Findings

New approaches outperform state-of-the-art methods

Content, relations, and activities all provide strong signals

Extensive experiments reveal data collection and transfer insights

Abstract

A large number of studies on social media compare the behaviour of users from different political parties. As a basic step, they employ a predictive model for inferring their political affiliation. The accuracy of this model can change the conclusions of a downstream analysis significantly, yet the choice between different models seems to be made arbitrarily. In this paper, we provide a comprehensive survey and an empirical comparison of the current party prediction practices and propose several new approaches which are competitive with or outperform state-of-the-art methods, yet require less computational resources. Party prediction models rely on the content generated by the users (e.g., tweet texts), the relations they have (e.g., who they follow), or their activities and interactions (e.g., which tweets they like). We examine all of these and compare their signal strength for the party prediction task. This paper lets the practitioner select from a wide range of data types that all give strong performance. Finally, we conduct extensive experiments on different aspects of these methods, such as data collection speed and transfer capabilities, which can provide further insights for both applied and methodological research.