Incidental data are data that people produce incidentally, as a byproduct of the normal course of operations of a platform, business, or government. Well-known examples include using Twitter, Facebook, Google search, smartphones, badges, etc. to study social phenomena, such as election behavior, attitudes, employment, or consumer confidence. It has been almost ten years since various high-impact papers and books have proclaimed the end of traditional social research and the beginning of a new era of exciting new possibilities for social science. In this talk, I review the evidence from the past decade or so regarding the value of incidental data for social research. This includes not only research in social science, but also in the humanities, data mining, and machine learning communities. While it is safe to say traditional social research has not ended, I conclude that incidental data may indeed allow for an "update" of (some of) social science. However, to accomplish this, a considerable amount of work is still needed. I give some suggestions for useful avenues for the work ahead, and how we can prepare ourselves for it.
29 May 2019 from 15:30 to 17:00
Incidental data for serious social research?
Cooperative Relations Seminar: lecture by Daniel Oberski
Start date and time29 May 2019 15:30
End date and time29 May 2019 17:00