A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit <a rel="external noopener" href="https://pure.uva.nl/ws/files/2639041/168273_495880.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Taking Stock of the Toolkit
<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>
Preserved Fulltext
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
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/21670811.2015.1096598">doi:10.1080/21670811.2015.1096598</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f2rqzpbl5vb4hjv5ikkudrhwle">fatcat:f2rqzpbl5vb4hjv5ikkudrhwle</a>
</span>
more »
... 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.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428072057/https://pure.uva.nl/ws/files/2639041/168273_495880.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/68/43/6843ab272cada3d50bc26b0258ef772a5cb5992f.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/21670811.2015.1096598">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
tandfonline.com
</button>
</a>