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Jill L. Drury, Amanda Anganes, Heather Byrne, Maria C. Casipe, Roger Dejean, Simone Hill, et al. (2012). Badge-primed decision making. In Z.Franco J. R. L. Rothkrantz (Ed.), ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management. Vancouver, BC: Simon Fraser University.
Abstract: We have been investigating new decision support methods for emergency responders. Most recently, we have added to our decision support prototype the concept of “badges”: symbols that cue decision makers to the top-ranked option(s) that are the recommended alternatives for a particular decision. This paper provides the rationale for badges, a description of the initial implementation, results from our first experiment with badges, and a discussion of the next steps. As a report on work-in-progress, the primary contribution of this paper is the description of the concept of badges and its proposed use for emergency response decision making. © 2012 ISCRAM.
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Muhammad Imran, Carlos Castillo, Jesse Lucas, Patrick Meier, & Jakob Rogstadius. (2014). Coordinating human and machine intelligence to classify microblog communications in crises. In and P.C. Shih. L. Plotnick M. S. P. S.R. Hiltz (Ed.), ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management (pp. 712–721). University Park, PA: The Pennsylvania State University.
Abstract: An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
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