Big Data & Society· 2015Q1
Adapting computational text analysis to social science (and vice versa)
- 226citations
- Q1SCImago
- 2015year
Short summary
Computational text analysis can improve social science by bridging differences in modeling approaches (unsupervised vs. supervised), causal inference, and algorithm development.
AI-generated from the title and abstract; the full text is not read.
Key points
- Social and computer scientists differ in text analysis approaches: unsupervised exploration vs. supervised training.
- Social scientists tend towards conventional causality, while computer scientists focus on developing new models.
- Bridging these perspectives can improve the practice and outcomes of social science research.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. Big Data & Society, 2015 · DOI ↗
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Field: General Social Sciences
General Social SciencesSocial Sciences