“You've got three days!” Case studies in field techniques for the time-challenged
Field methods casebook for software design
Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
The Journal of Machine Learning Research
Destabilization of covert networks
Computational & Mathematical Organization Theory
Introduction to Information Retrieval
Introduction to Information Retrieval
Conditional random fields for entity extraction and ontological text coding
Computational & Mathematical Organization Theory
Topic modeling for OLAP on multidimensional text databases: topic cube and its applications
Statistical Analysis and Data Mining - Best of SDM'09
Mining advisor-advisee relationships from research publication networks
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
PET: a statistical model for popular events tracking in social communities
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Probabilistic latent semantic analysis
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
A framework for schema-driven relationship discovery from unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Computational & Mathematical Organization Theory
Near real time assessment of social media using geo-temporal network analytics
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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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.