Abstract
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.
Highlights
The use of social media is ubiquitous and, while the services and platforms used may vary, their overarching goal is the same: to “allow the creation and exchange of User Generated Content” (Kaplan and Haenlein 2010)
Instead we focus on the two key elements for our system: the taxonomies used to categorise and extract entities that form the basis of our own context-based entity extraction and the mapping and visualisation functionalities that will take the formal concept analysis (FCA) output and present it via a dashboard interface
There are few applications that use this type of entity extraction, with most relying on machine learning approaches; we feel that this approach gives us greater control over the entities extracted and is advantageous for FCA
Summary
The use of social media is ubiquitous and, while the services and platforms used may vary, their overarching goal is the same: to “allow the creation and exchange of User Generated Content” (Kaplan and Haenlein 2010). We focus on its potential applications to crisis situations and, in particular, the complete process from data acquistion and information extraction to visualisation and analysis. Because of this ubiquity, during a crisis people naturally reach for their smartphones to report, comment and explore information surrounding the crisis creating a large volume of social media data. In this paper we propose a workflow for extracting social media data and from it producing and visualising aggregated reports about specific events during a crisis Bearing this in mind, we explain and develop a multi-stage process, set against the backdrop of real crisis tweets from the Nepal earthquake of 2015, which demonstrates how we can go from raw social media data to aggregated and specific crisis concepts explorable through a dashboard interface.
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