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Computational analysis of communication : a practical introduction to the analysis of texts, networks, and images with code examples in Python and R

By: Atteveldt, Wouter van.
Contributor(s): Trilling, Damian | Calderon, Carlos Arcila.
Publisher: Hoboken : Wiley Blackwell, 2022Description: xiii, 314 p. ; ill., 26 cm.ISBN: 9781119680239.Subject(s): Social sciences | Network analysis | Communication | Data processing | Computational linguisticsDDC classification: 302.3072 Summary: The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before.
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Includes bibliographical references and index.

The use of computers is nothing new in the social sciences. In fact, one could argue that some disciplines within the social sciences have even be early adopters of computational approaches. Take the gathering and analyzing of large-scale survey data, dating back until the use of the Hollerith Machine in the 1890 US census. Long before every scholar had a personal computer on their desk, social scientists were using punch cards and mainframe computers to deal with such data. If we think of the analysis of communication more specifically, we see attempts to automate content analysis already in the 1960's [see, e.g. Scharkow, 2017]. Yet, something has profoundly changed in the last decades. The amount and kind of data we can collect as well as the computational power we have access to have increased dramatically. In particular, digital traces that we leave when communicating online, from access logs to comments we place, have required new approaches [e.g., Trilling, 2017]. At the same time, better computational facilities now allow us to ask questions we could not answer before.

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