The rise of digital media has witnessed a paradigmatic shift in the way that media outlets conceptualize and classify their audience. Whereas during the era of mass media, ‘seeing’ the audience was based on a scientific episteme combining social theory and empirical research, with digital media ‘seeing’ the audience has come to be dominated by a new episteme, based on big data and algorithms. This article argues that the algorithmic episteme does not see the audience more accurately, but differently. Whereas the scientific episteme upheld an ascriptive conception which assigned individuals to a particular social category, the algorithmic episteme assumes a performative individual, based on behavioral data, sidestepping any need for a theory of the self. Since the way in which the media see their audience is constitutive, we suggest that the algorithmic episteme represents a new way to think about human beings.
|Number of pages||16|
|Journal||Media, Culture and Society|
|State||Published - 1 Nov 2019|
Bibliographical noteFunding Information:
Research for this article was supported by a grant of from the Israel Science Foundation No. 696/16.
© The Author(s) 2019.
Copyright 2019 Elsevier B.V., All rights reserved.
- big data
- digital media
- mass media