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During a crisis citizens reach for their smart phones to report, comment and explore information surrounding the crisis. These actions often involve social media and this data forms a large repository of real-time, crisis related information. Law enforcement agencies and other first responders see this information as having untapped potential. That is, it has the capacity extend their situational awareness beyond the scope of a usual command and control centre. Despite this potential, the sheer volume, the speed at which it arrives, and unstructured nature of social media means that making sense of this data is not a trivial task and one that is not yet satisfactorily solved; both in crisis management and beyond. Therefore we propose a multi-stage process to extract meaning from this data that will provide relevant and near real-time information to command and control to assist in decision support. This process begins with the capture of real-time social media data, the development of specific LEA and crisis focused taxonomies for categorisation and entity extraction, the application of formal concept analysis for aggregation and corroboration and the presentation of this data via map-based and other visualisations. We demonstrate that this novel use of formal concept analysis in combination with context-based entity extraction has the potential to inform law enforcement and/or humanitarian responders about on-going crisis events using social media data in the context of the 2015 Nepal earthquake.

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A number of crisis situations, such as natural disasters have affected the planet over the last decade. The outcomes of such disasters are catastrophic for the infrastructures of modern societies. Furthermore, after large disasters, societies come face-to-face with important issues, such as the loss of human lives, people who are missing and the increment of the criminality rate. In many occasions, they seem unprepared to face such issues. This paper aims to present an automated system for the synchronization of the police and Law Enforcement Agencies (LEAs) for the prevention of criminal activities during and post a large crisis situation. The paper presents a review of the literature focusing on the necessity of using social media and crowd-sourcing data mining techniques in combination with advanced web technologies for resolving problems related to criminal activities caused during and after a crisis. The focus of the paper is the ATHENA Crisis Management system which uses a number of data mining techniques to collect and analyze crisis-related data from social media for the purpose of crime prevention. Its main strength is the combined use of a variety of data mining algorithms through a number of interfaces for the purpose of extracting useful social media information related to criminal activities during and after a large crisis. Conclusions are drawn on the significance of social media and crowd-sourcing data mining techniques for the resolution of problems related to large crisis situations with emphasis to the ATHENA system.

The analysis of potentially large volumes of crowd-sourced and social media data is central to meeting the requirements of the Athena project. Here, we discuss the various stages of the pipeline process we have developed, including acquisition of the data, analysis, aggregation, filtering and structuring. We highlight the challenges involved when working with unstructured, noisy data from sources such as Twitter, and describe the crisis taxonomies that have been developed to support the tasks and enable concept extraction. State of the art technology such as formal concept analysis and machine learning is used to create a range of capabilities including concept drill down, sentiment analysis, credibility assessment and assignment of priority. We present an evaluation of results obtained from a set of tweets which emerged from the Colorado wild fires of 2012.

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Abstract-The number and intensity of crisis incidents that have happened worldwide in the last decade, such as the Haiti earthquake and the Mumbai bombings, have revealed the need for an organized system to support search and rescue operations. This paper presents such a system and focuses on one of its central elements, the Crisis Management Command and Control Intelligence Dashboard (CCCID). The paper presents the Service-Oriented Architecture (SOA) approach that was followed for the design of the dashboard and explains its specialized functionalities. A number of conclusions are drawn in relation to the efficiency of the dashboard for crisis management.

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