Taking Stock of the Toolkit

Abstract

When analyzing digital journalism content, journalism scholars are confronted with a number of substantial differences compared to traditional journalistic content. The sheer amount of data and the unique features of digital content call for the application of valuable new techniques. Various other scholarly fields are already applying computational methods to study digital journalism data. Often, their research interests are closely related to those of journalism scholars. Despite the advantages that computational methods have over traditional content analysis methods, they are not commonplace in digital journalism studies. To increase awareness of what computational methods have to offer, we take stock of the toolkit and show the ways in which computational methods can aid journalism studies. Distinguishing between dictionary-based approaches, supervised machine learning, and unsupervised machine learning, we present a systematic inventory of recent applications both inside as well as outside journalism studies. We conclude with suggestions for how the application of new techniques can be encouraged.

Keywords:

• automated content analysis
• computational social science
• digital data
• journalism studies
• review

Disclosure Statement

No potential conflict of interest was reported by the authors.

Notes

1. A common example is the tf–idf (term frequency–inverse document frequency) scheme, in which the frequency of a term in a given document is weighted by the number of documents in which it occurs.

2. The name of the program stands for Linguistic Inquiry and Word Count.

3. These are variables that are directly observable (like number of words or is actor X mentioned?), and, unlike abstract concepts as tone or frame, are not open to multiple interpretations.