Adapting computational text analysis to social science (and vice versa)
Author(s) -
Paul DiMaggio
Publication year - 2015
Publication title -
big data and society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.244
H-Index - 37
ISSN - 2053-9517
DOI - 10.1177/2053951715602908
Subject(s) - computational sociology , field (mathematics) , perspective (graphical) , data science , computer science , focus (optics) , discipline , artificial intelligence , sociology , social science , physics , mathematics , pure mathematics , optics
Social scientists and computer scientist are divided by small differences in perspective and not by any significant disciplinary divide. In the field of text analysis, several such differences are noted: social scientists often use unsupervised models to explore corpora, whereas many computer scientists employ supervised models to train data; social scientists hold to more conventional causal notions than do most computer scientists, and often favor intense exploitation of existing algorithms, whereas computer scientists focus more on developing new models; and computer scientists tend to trust human judgment more than social scientists do. These differences have implications that potentially can improve the practice of social science
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