Teaching the computer to code frames in news: comparing two supervised machine learning approaches to frame analysis

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.