TAKING STOCK OF THE TOOLKIT An overview of relevant automated content analysis approaches and techniques for digital journalism scholars
TAKING STOCK OF THE TOOLKIT An overview of relevant automated content analysis approaches and techniques for digital journalism scholars
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DOI：
10.1080/21670811.2015.1096598
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发表时间：
2016-01-01
影响因子：
5.4
通讯作者：
Trilling, Damian
中科院分区：
文献类型：
--
作者：
Boumans, Jelle W.;Trilling, Damian
在分析数字新闻内容时，新闻学者面临着与传统新闻内容相比的许多实质性差异。数字内容的数据量和独特性要求应用有价值的新技术。其他各种学术领域已经在应用计算方法来研究数字新闻数据。通常，他们的研究兴趣与新闻学者的研究兴趣密切相关。尽管计算方法比传统的内容分析方法具有优势，但它们在数字新闻研究中并不常见。为了提高人们对计算方法的认识，我们对工具包进行了评估，并展示了计算方法可以帮助新闻学研究的方法。区分基于字典的方法，监督机器学习和无监督机器学习，我们提出了一个系统的清单，最近的应用程序内部以及外部新闻研究。最后，我们提出了如何鼓励新技术的应用的建议。
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.