Teaching the Computer to Code Frames in News: Comparing Two Supervised Machine Learning Approaches to Frame Analysis

Björn Burscher*, Daan Odijk, Rens Vliegenthart, Maarten de Rijke, Claes H. de Vreese

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

58 Citations (Scopus)

Abstract

We explore the application of supervised machine learning (SML) to frame coding. By automating the coding of frames in news, SML facilitates the incorporation of large-scale content analysis into framing research, even if financial resources are scarce. This furthers a more integrated investigation of framing processes conceptually as well as methodologically. We conduct several experiments in which we automate the coding of four generic frames that are operationalised as a set of indicator questions. In doing so, we compare two approaches to modelling the coherence between indicator questions and frames as an SML task. The results of our experiments show that SML is well suited to automate frame coding but that coding performance is dependent on the way SML is implemented.

Original languageEnglish
Pages (from-to)190-206
Number of pages17
JournalCommunication Methods and Measures
Volume8
Issue number3
DOIs
Publication statusPublished - Jul 2014
Externally publishedYes

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