000			a
999			_c30814 _d30814
008			220616b xxu||||| |||| 00| 0 eng d
020			_a9781119680239
082			_a302.3072 _bATT
100			_aAtteveldt, Wouter van
245			_aComputational analysis of communication : a practical introduction to the analysis of texts, networks, and images with code examples in Python and R
260			_bWiley Blackwell, _c2022 _aHoboken :
300			_axiii, 314 p. ; _bill., _c26 cm
365			_b49.95 _cUSD _d81.20
504			_aIncludes bibliographical references and index.
520			_aThe 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.
650			_aSocial sciences
650			_aNetwork analysis
650			_aCommunication
650			_aData processing
650			_aComputational linguistics
700			_aTrilling, Damian
700			_aCalderon, Carlos Arcila
942			_2ddc _cBK