Raw data or hypersymbols? Meaning-making with digital data, between discursive processes and machinic procedures

Abstract

AbstractThe large-scale and intensive collection and analysis of digital data (commonly called “Big Data”) has become a common, popular, and consensual research method for the social sciences, as the automation of data collection, mathematization of analysis, and digital objectification reinforce both its efficiency and truth-value. This article opens with a critical review of the literature on data collection and analysis, and summarizes current ethical discussions focusing on these technologies. A semiotic model of data production and circulation is then introduced to problematize the view that digital data has ceased to stand for a formalization method (a possible kind of representation among others), and effectively “becomes the world itself” (a direct presentation of the world outperforming all other modes of representation). Following Charles Sanders Peirce’s semiotics and pragmaticist philosophy, we characterize digitalization as a hypersymbolic semiotic process, and we highlight the naturalization of meaning, the illusion of iconicity, and rhetorical efficiency on which data’s truth value relies within the context of its large-scale, profit-driven, and results-oriented research uses. This outlines some epistemological and ethical implications of data’s visualization, use, and authority, and indicates avenues for critical semiotics of contemporary data science and analysis.