As a Nation we must maintain a state of readiness to respond to both natural disasters and man-made threats. 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Disaster management (or emergency management) is the term used to designate the efforts of communities or businesses to plan for and coordinate all the personnel and materials required to either mitigate the effects of, or recover from, natural or man-made disasters, or acts of terrorism.
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Digitalization and the rise of social media have led disaster management to the insight that modern information technology will have to play a key role in dealing with a crisis. In this context, the paper introduces a NLP software for social media text analysis that has been developed in cooperation with disaster managers in the European project Slandail. The aim is to show how state-of-the-art techniques from text mining and information extraction can be applied to fulfil the requirements of the end-users. By way of example use cases the capacity of the approach will be demonstrated to make available social media as a valuable source of information for disaster management.
The emerging field of crisis informatics (e.g., Palen et al. (2010)) is driven by the insight that, in the digital age, the ability to efficiently access and process huge amounts of unstructured data is crucial to situational awareness, knowledge building, and decision-making of organizations responsible for saving lives and property of people affected by a crisis. Disaster events like hurricane Katrina, 9/11, the Haiti earthquake, or the Central-European Flooding 2013 have demonstrated that there is urgent need to understand how information is shared during a crisis and to improve strategies and technologies for turning information into relevant insights and timely actions. Within crisis informatics, social media offer an interesting new opportunity for improvement of disaster management by providing fast, interactive communication channels and enabling participation of the public (Starbird and Palen 2011). However, social media data are big data in terms of volume, velocity, variety and veracity, and, accordingly, the demands and challenges with respect to the development of appropriate information technologies are especially high.
The paper presents possibilities for social media analysis that arise within a disaster management software that has been developed as part of the Slandail project (Slandail 2014), funded by the European community. Slandail deals with data in different modalities (texts and images) and languages (English, German and Italian) as well as with the integration of cross-lingual and cross-cultural aspects of crisis communications and has a special focus on issues related to the legal and ethical correctness of data use. End-users from Ireland, Germany, and Italy have been involved in the development of the system from design to testing.
The focus of the paper is on text analysis functionalities using NLP methods from the fields of text mining and information extraction that have been contributed by the two German partner organizations in cooperation with the disaster control authorities Landeskommando and Bezirksverbindungskommando in Saxony. The prototype of the software (Topic Analyst) has been implemented at CID and further developed in cooperation with InfAI during the course of the Slandail project. The software module is currently under consideration by German authorities for future use in German disaster management.
Computational methods from NLP offer a wide variety of possibilities for systematically and efficiently searching, filtering, sorting and analyzing huge amounts of data and thereby can enable end-users from disaster management to face the problem of information overload posed by social media. In this section, we describe how interests on the side of disaster management have guided our choice of the methods used and our way to apply them in context of our software.
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