Editorial analytics : how a U.S. newspaper applies data to match target audiences
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This research is an in-depth case study of a major regional U.S. daily embracing audience data under the pressure of limited resources and shrinking advertising budgets. The legacy news operation observes analytics through "the rearview mirror," believing that it is neither a true measure of audience engagement nor journalistic quality. Experiencing first-hand the implications of a commodified consumer attention, newsmakers find themselves conflicted between exercising their traditional role of public service and them leaning toward "soft" news to drive higher page-view revenue. By applying the actor-network theory as its central theoretical framework, the study addresses an intricate interplay of day-to-day editorial decision-making, Big Data analytics and the market economics of evolving digital news business.
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License.