Creating corroborated crisis reports from social media data through formal concept analysis

ANDREWS, Simon, GIBSON, Helen, DOMDOUZIS, Konstantinos and AKHGAR, Babak (2016). Creating corroborated crisis reports from social media data through formal concept analysis. Journal of Intelligent Information Systems, 47, 287-312.

Preview

PDF Andrews Creating corroborated.pdf - Published Version Creative Commons Attribution. Download (3MB) | Preview

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. Keywords : formal concept analysis, crisis management, disaster response, visualisation, entity extraction.

Item Type:	Article
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
Departments - Does NOT include content added after October 2018:	Faculty of Science, Technology and Arts > Department of Computing
Identification Number:	https://doi.org/10.1007/s10844-016-0404-9
Page Range:	287-312
Depositing User:	Helen Gibson
Date Deposited:	25 Apr 2016 14:47
Last Modified:	18 Mar 2021 04:19
URI:	https://shura.shu.ac.uk/id/eprint/12081

Actions (login required)

View Item

Downloads

Publication Metrics