Browsing by Subject "computational social science"

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    Editorial Judgment in an Age of Data: How Audience Analytics and Metrics are Influencing the Placement of News Products
    (2015-05) Zamith, Rodrigo
    In recent years, audience analytics (systems that collect and analyze digital trace data from users) and audience metrics (quantified measures of how content is consumed and interacted with) have gained currency within newsrooms, enabling them to influence editorial newsworkers' constructions of their audiences and, consequently, the shapes that news products take. The extent of that influence, however, remains largely unknown, with few studies examining the direct relationship between metrics and news content. The present work addresses this shortcoming by offering methodological guidance and an empirical assessment of the extent to which one particularly salient metric, page views, influences the prominence and de-selection of news items on the homepages of several news organizations. It demonstrates that algorithms can be leveraged to computationally analyze particular aesthetics of a large volume of homepages; that the�most viewed' list can serve as a useful indicator of the popularity of news items, though such lists are not always comparable across organizations and introduce important limitations; and that the effect of an item's popularity on its subsequent placement on the homepage is fairly muted in the process of selection, though it is greater in the process of de-selection. In short, the present research indicates that the current content-related effects of audience metrics--at least as it pertains to a particular editorial function and metric-- may be overstated in the literature, and offers pathways for further studying similar relationships.