Digital Habitus or Personalization without Personality

This article aims to offer an original framework to understand the ontological structure of digital media and technologies, along with their effects of subjectivation. In the first section, we confront Bourdieu’s and Latour’s social theories. Indeed, Latour and Bourdieu offered two almost opposite social theories, and both of them can be used to understand digital media and technologies. Our hypothesis is that the digital of today is less Latourian than Bourdieusian. In the second section, we introduce the concept of digital habitus. In particular, we contend that digital machines such as algorithms of machine learning are habitus machines. Although their results present a greater granularity with respect to the standard techniques of the past, these algorithms still reduce individuals to categories, general trends, classes, and behaviors. Such a reduction has flattening effects on the individuals’ self-understanding, especially in terms of identity and interaction with the social world. This is the phenomenon described as the “personalization without personality.” In the third section, we look for proof of our previous insights through a qualitative and comparative analysis between three kinds of data and information visualization. More specifically, we show that contemporary techniques for data visualization with machine learning algorithms are closer to Bourdieu’s use of correspondence analysis (CA) and the multiple correspondence analysis (MCA) than to Latour-inspired network visualizations.