A social network map of 2,200 people, the largest group of connected individuals in the Framingham Heart Study, in the year 2000. Each circle represents one person, and the size of each circle is proportional to that person's body-mass index (BMI). Yellow circles indicate people who are considered medically obese and green circles indicate people who are not obese.
Lines indicate family and friendship ties.
Figure courtesy of James Fowler, UC San Diego.
Figure courtesy of James Fowler, UC San Diego.
L'étude ci-dessus (encore qu'on soit plutôt dans le "small data") et les conclusions qui en sont tirées pourraient servir d'illustration au papier de boyd et Crawford ... au milieu de milliers d'autres.
"In this essay, we are offering six provocations that we hope can spark conversations about the issues of Big Data. Social and cultural researchers have a stake in the computational culture of Big Data precisely because many of its central questions are fundamental to our disciplines. Thus, we believe that it is time to start critically interrogating this phenomenon, its assumptions, and its biases."
- Automating Research Changes the Definition of Knowledge
- Claims to Objectivity and Accuracy are Misleading
- Bigger Data are Not Always Better Data
- Not All Data Are Equivalent
- Just Because it is Accessible Doesn’t Make it Ethical
- Limited Access to Big Data Creates New Digital Divides
L'article complet est accessible en ligne, ici. Le blog de danah boyd est depuis ses débuts une mine pour ceux qui s'intéressent à l'expansion du monde digital.
