Categorizing political campaign messages on social media using supervised machine learning

Stromer-Galley, J. and Rossini, P. (2023) Categorizing political campaign messages on social media using supervised machine learning. Journal of Information Technology and Politics, (doi: 10.1080/19331681.2023.2231436) (Early Online Publication)

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.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Rossini, Dr Patricia
• Authors: Stromer-Galley, J., and Rossini, P.
• College/School: College of Social Sciences > School of Social and Political Sciences > Politics
• Journal Name: Journal of Information Technology and Politics
• Publisher: Taylor & Francis
• ISSN: 1933-1681
• ISSN (Online): 1933-169X
• Published Online: 09 July 2023
• Copyright Holders: Copyright © 2023 Taylor and Francis
• First Published: First published in Journal of Information Technology and Politics 2023
• Publisher Policy: Reproduced in accordance with the publisher copyright policy

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