Data-to-model: a mixed initiative approach for rapid ethnographic assessment

Abstract

Rapid ethnographic assessment is used when there is a need to quickly create a socio-cultural profile of a group or region. While there are many forms such an assessment can take, we view it as providing insight into who are the key actors, what are the key issues, sentiments, resources, activities and locations, how have these changed in recent times, and what roles do the various actors play. We propose a mixed initiative rapid ethnographic approach that supports socio-cultural assessment through a network analysis lens. We refer to this as the data-to-model (D2M) process. In D2M, semi-automated computer-based text-mining and machine learning techniques are used to extract networks linking people, groups, issues, sentiments, resources, activities and locations from vast quantities of texts. Human-in-the-loop procedures are then used to tune and correct the extracted data and refine the computational extraction. Computational post-processing is then used to refine the extracted data and augment it with other information, such as the latitude and longitude of particular cities. This methodology is described and key challenges illustrated using three distinct data sets. We find that the data-to-model approach provides a reusable, scalable, rapid approach for generating a rapid ethnographic assessment in which human effort and coding errors are reduced, and the resulting coding can be replicated.