Researching Culture through Big Data: Computational Engineering and the Human and Social Sciences

Teresa Duarte Martinho
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Teresa Duarte Martinho: Universidade de Lisboa, Instituto de Ciências Sociais, Av. Professor Aníbal de Bettencourt 9, 1600-189 Lisboa, Portugal

Social Sciences, 2018, vol. 7, issue 12, 1-17

Abstract: The emergence of big data and data science has caused the human and social sciences to reconsider their aims, theories, and methods. New forms of inquiry into culture have arisen, reshaping quantitative methodologies, the ties between theory and empirical work. The starting point for this article is two influential approaches which have gained a strong following, using computational engineering for the study of cultural phenomena on a large scale: ‘distant reading’ and ‘cultural analytics’. The aim is to show the possibilities and limitations of these approaches in the pursuit of scientific knowledge. The article also focuses on statistics of culture, where integration of big data is challenging procedures. The article concludes that analyses of extensive corpora based on computing may offer significant clues and reveal trends in research on culture. It argues that the human and social sciences, in joining up with computational engineering, need to continue to exercise their ability to perceive societal issues, contextualize objects of study, and discuss the symbolic meanings of extensive worlds of artefacts and discourses. In this way, they may help to overcome the perceived restrictions of large-scale analysis such as the limited attention given to individual actors and the meanings of their actions.

Keywords: human and social sciences; data science; big data; distant reading; cultural analytics; statistics of culture; positivism/interpretivism/interactionism (search for similar items in EconPapers)
JEL-codes: A B N P Y80 Z00 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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