The Mediated Data Model of Communication Flow: Big Data and Data Journalism

Abstract

In recent decades, journalism has undergone considerable transformation, initially fuelled by the digitalization of journalistic work flows and subsequently by the introduction of the Internet, its services, and its effects. Since contemporary journalists employ multiple digital tools and services to gather, administrate, and process information for public consumption, new types/genres of journalism have emerged. Among these, data journalism is one of the most prominent, introduced due to the availability of data in digital form and also to the abundance of efficient online tools that help users analyze, visualize, and publish large amounts of data. Indeed, it is not only the journalistic profession that has changed, but the communication process itself, which has been fundamentally altered to meet the public's current needs and demands. This paper introduces and examines the mediated data model of communication flow to describe these new norms in the mass communication process. Using big data as a case study and moving on to data journalism, we provide a theoretical overview of the model, employing the theory of the two-step flow of communication as a starting point, while attempting to shed light on the current communication process between journalists/media and their initial sources of information.

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