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.
Highlights
The emergence and dissemination of big data, together with the consolidation of information as a major category of thought, has produced for the human and social sciences a period of questioning and reconsideration of many of their objectives, theories, and methods (Savage and Burrows 2007)One of the consequences of the accessibility of the expanding amount of data generated by the new information technologies was that digital systems became the driving force for a new way of producing scientific knowledge
This study aims to demonstrate the possibilities and limitations of ‘distant reading’, ‘cultural analytics’, and statistics which use big data in producing scientific knowledge
This article has sought to analyze the implications of using big data in procedures, theories, concepts, and discoveries produced in research in the human and social sciences, in connection with the study of cultural phenomena and artefacts which are increasingly stored in digital form
Summary
The emergence and dissemination of big data, together with the consolidation of information as a major category of thought, has produced for the human and social sciences a period of questioning and reconsideration of many of their objectives, theories, and methods (Savage and Burrows 2007). Another form of macro analysis of the culture field, mainly based on analyzing images, is the tendency known as ‘cultural analytics’, an expression coined in 2005 by Lev Manovich This approach, which seeks to combine computational engineering, the history of art, and sociology, favors using massive content generated and stored through digital interfaces, paying special attention to images published and disseminated through social networks (Manovich 2018). The incorporation of big data is challenging and reshaping procedures for handling quantitative data and producing scientific knowledge, revealing a shift in the epistemic values related to data modeling objectives (Pietsch 2013) In addition to these commonalities, statistics are significant because they are a source used by social scientists, above all those who study cultural practices and consumption in a broader spatial and temporal perspective, with a view to determining trends (Christin and Donnat 2014). The conclusion offers an overall view of the different sections of the article, bringing them together for a considered, integrated view of the scope and limitations of the linkages between the study of culture in the human and social sciences by means of big data and its ties to computational engineering
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