Paper submitted to EPLO Convention, 2014
Abstract: Crises and disasters occur all over the world with the highest impact on the most vulnerable in society. Generating a thorough and trusted status of information about the situation is apriority for effective and coordinated disaster management and relief measures delivered by governmental organizations (GOs) and non-governmental organizations (NGOs) in the surroundings of a critical event. . Information gathering, processing, visualization and (internal as well as external) dissemination for decision support and mitigation is performed via a number of different channels and media, among them various social media channels. The QuOIMA-project, funded by the Austrian Security Research Program KIRAS by the Austrian Ministry of Transport, Innovation and Technology, focuses on the various possibilities to use publicly available, open source data generated in the sphere of traditional (online distributed) and social media. These data can be used on the one hand as a vital input for situation awareness and decision support of disaster management; on the other hand they can be used to initiate and maintain active, bidirectional, participatory involvement of community members in case of a serious event – under the prerequisite precondition of a trusted, reliable, and privacy safeguarding framework. Subsequently, relevant core issues of privacy rights and the Privacy Impact Assessment applied in the course of the project QuOIMA are discussed. But even before crises occur, precautions can be taken by monitoring social media sources such as Facebook, Twitter or specific blogs. Risk indicators can be identified more quickly, structures and work plans for disaster management can be set up. One of those means that will be outlined in this context is the new and emerging crowd tasking approach, a promising and high potential area for the involvement of community members.
This article was presented at the EPLO Convention 2014. To access the full article, please fill in the form below.
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