Taking Stock of the Toolkit

Jelle W. Boumans, Damian Trilling
<span title="2015-11-03">2015</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2s4mq34wvf47mahxvibk4f7wy" style="color: black;">Digital Journalism</a> </i> &nbsp;
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
more &raquo; ... s 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.
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