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Computational text analysis has become an exciting research field with many applications in communication research. It can be a difficult method to apply, however, because it requires knowledge of various techniques, and the software required to perform most of these techniques is not readily available in common statistical software packages. In this teacher's corner, we address these barriers by providing an overview of general steps and operations in a computational text analysis project, and demonstrate how each step can be performed using the R statistical software. As a popular open-source platform, R has an extensive user community that develops and maintains a wide range of text analysis packages. We show that these packages make it easy to perform advanced text analytics.

On newspaper websites, journalists can observe the preferences of the audience in unprecedented detail and for low costs, based on the audience clicks (i.e. page views) for specific news articles. This article addresses whether journalists use this information to cater to audience preferences in their news selection choices. We analyzed the print and online editions of five national newspapers from the Netherlands with a mixed-method approach. Using a cross-lagged analysis covering 6 months, we found that storylines of the most-viewed articles were more likely to receive attention in subsequent reporting, which indicates that audience clicks affect news selection. However, based on interviews with editors we found that they consider the use of

a b s t r a c tMeaning can be generated when information is related at a systemic level. Such a system can be an observer, but also a discourse, for example, operationalized as a set of documents. The measurement of semantics as similarity in patterns (correlations) and latent variables (factor analysis) has been enhanced by computer techniques and the use of statistics; for example, in "latent semantic analysis". This communication provides an introduction, an example, pointers to relevant software, and summarizes the choices that can be made by the analyst. Visualization ("semantic mapping") is thus made more accessible.

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