In the article, we argue that the advent of data mining techniques and big data in media and communication studies present problems that involve fundamental methodological questions, requiring us to revisit existing ways in which the link between theory, operationalization and data are explained and justified. We note that the discourse of instrumental optimization that surrounds big data clouds epistemic debates about their appropriate integration in scholarly explanations, and argue that a discussion of these problems can usefully depart from a distinction between the two main types of data mining models (supervised and unsupervised). We argue that both types pose specific challenges and give examples of ways they have been productively overcome. In particular, we argue that while big data approaches have introduced novel opportunities for research, they have fundamentally been incorporated into media and communication studies in ways that comply with existing, prototypical explanatory schemes. Our examples link specific empirical studies to general strategies of scientific explanation, focusing on neo-positivist, critical realist and interpretivist explanations.

中文翻译：

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大数据与解释：对大数据在媒体和传播研究中的应用的思考

在本文中，我们认为媒体和传播研究中数据挖掘技术和大数据的出现提出了涉及基本方法论问题的问题，要求我们重新审视解释和证明理论、操作和数据之间联系的现有方式。我们注意到，围绕大数据的工具优化的论述使关于它们在学术解释中的适当整合的认知辩论变得更加复杂，并认为对这些问题的讨论可以有效地摆脱两种主要类型的数据挖掘模型（监督和无监督）之间的区别。 ）。我们认为这两种类型都带来了特定的挑战，并举例说明了如何有效地克服它们。特别是，我们认为，虽然大数据方法为研究带来了新的机会，但它们已从根本上以符合现有原型解释方案的方式被纳入媒体和传播研究。我们的例子将具体的实证研究与科学解释的一般策略联系起来，重点是新实证主义、批判现实主义和解释主义解释。