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Automated Coding of Political Campaign Advertisement Videos: An Empirical Validation Study

Published online by Cambridge University Press:  10 November 2022

Alexander Tarr Open the ORCID record for Alexander Tarr [Opens in a new window] ,
June Hwang  and
Kosuke Imai Open the ORCID record for Kosuke Imai [Opens in a new window]
Alexander Tarr
Affiliation:
Graduate Student, Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA. E-mail: atarr3@gmail.com
June Hwang
Affiliation:
Advisor, Consulate General of the Republic of Korea, Honolulu, HI 96817, USA. E-mail: wjhwangusa@gmail.com
Kosuke Imai*
Affiliation:
Professor, Department of Government and Department of Statistics, Harvard University, Cambridge, MA 02138, USA. E-mail: imai@harvard.edu, URL: https://imai.fas.harvard.edu
*
Corresponding author Kosuke Imai
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Abstract

Video advertisements, either through television or the Internet, play an essential role in modern political campaigns. For over two decades, researchers have studied television video ads by analyzing the hand-coded data from the Wisconsin Advertising Project and its successor, the Wesleyan Media Project (WMP). Unfortunately, manually coding more than a hundred of variables, such as issue mentions, opponent appearance, and negativity, for many videos is a laborious and expensive process. We propose to automatically code campaign advertisement videos. Applying state-of-the-art machine learning methods, we extract various audio and image features from each video file. We show that our machine coding is comparable to human coding for many variables of the WMP datasets. Since many candidates make their advertisement videos available on the Internet, automated coding can dramatically improve the efficiency and scope of campaign advertisement research. Open-source software package is available for implementing the proposed methodology.

Keywords

audio datacomputer visionimage datamachine learningtext datavideo data

Information

Type
Article
Information
Political Analysis , Volume 31 , Issue 4 , October 2023 , pp. 554 - 574
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the Society for Political Methodology

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Footnotes

Edited by Jeff Gill

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