Using Supervised Machine Learning to Code Policy Issues: Can Classifiers Generalize across Contexts?

Bjorn Burscher, Rens Vliegenthart, Claes H. De Vreese

Research output: Contribution to journalArticleAcademicpeer-review

49 Citations (Scopus)

Abstract

Content analysis of political communication usually covers large amounts of material and makes the study of dynamics in issue salience a costly enterprise. In this article, we present a supervised machine learning approach for the automatic coding of policy issues, which we apply to news articles and parliamentary questions. Comparing computer-based annotations with human annotations shows that our method approaches the performance of human coders. Furthermore, we investigate the capability of an automatic coding tool, which is based on supervised machine learning, to generalize across contexts. We conclude by highlighting implications for methodological advances and empirical theory testing.

Original languageEnglish
Pages (from-to)122-131
Number of pages10
JournalAnnals of the American Academy of Political and Social Science
Volume659
Issue number1
DOIs
Publication statusPublished - 15 May 2015
Externally publishedYes

Keywords

  • agenda setting
  • big data
  • content analysis
  • machine learning

Fingerprint

Dive into the research topics of 'Using Supervised Machine Learning to Code Policy Issues: Can Classifiers Generalize across Contexts?'. Together they form a unique fingerprint.

Cite this