Categorizing political campaign messages on social media using supervised machine learning

Abstract

ABSTRACT Scholars have access to a rich source of political discourse via social media. Although computational approaches to understand this communication are being used, they tend to be unsupervised and off-the-shelf algorithms to describe a corpus of messages. This article details our approach at using human-supervised machine learning to study political campaign messages. Although some declare this technique too labor-intensive, it provides theoretically informed classification, making it more accurate and reliable. This article describes the design decisions and accuracy of our algorithms, and the applicability of the approach to classifying messages from Facebook and Twitter across two cultures and to advertisements.