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Avelino F. Gomes Filho, André L. A. Sobral, Claudio A. Passos, Arce, D., Gustavo A. Bianco, Júlio C. Rodrigues, et al. (2014). C2 Center dealing with the unexpected: Resilience and brittleness during FIFA confederation cup. 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. 100–109). University Park, PA: The Pennsylvania State University.
Abstract: Forecast and plan response to incidents are fundamental to create a Command and Control Center (C2 Center). However, some incidents are considered chaotic and are completely understood only after happening. These unforeseen incidents pose challenges to plans of such centers and if not properly managed, may result in failures. This article describes how the Integrated C2 Center of Rio de Janeiro City (CICC-RJ) responds to violent, unexpected and improbable events, especially related to protests that took place during the 2013 FIFA Confederations Cup. It aims to describe from the resilience engineering point of view how the CICC-RJ function to cope with incidents, where the structure has proved to be resilient, where it holds brittleness, and to suggest possible actions to help the center to become more resilient to upcoming events.
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Kate Starbird, Grace Muzny, & Leysia Palen. (2012). Learning from the crowd: Collaborative filtering techniques for identifying on-the-ground Twitterers during mass disruptions. 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: Social media tools, including the microblogging platform Twitter, have been appropriated during mass disruption events by those affected as well as the digitally-convergent crowd. Though tweets sent by those local to an event could be a resource both for responders and those affected, most Twitter activity during mass disruption events is generated by the remote crowd. Tweets from the remote crowd can be seen as noise that must be filtered, but another perspective considers crowd activity as a filtering and recommendation mechanism. This paper tests the hypothesis that crowd behavior can serve as a collaborative filter for identifying people tweeting from the ground during a mass disruption event. We test two models for classifying on-the-ground Twitterers, finding that machine learning techniques using a Support Vector Machine with asymmetric soft margins can be effective in identifying those likely to be on the ground during a mass disruption event. © 2012 ISCRAM.
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