Online indexing and clustering of social media data for emergency management
Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.
Journal: Neurocomputing
Publisher: Elsevier
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.01.084
Abstract:Social media becomes a vital part in our daily communication practice, creating a huge amount of data and covering different real-world situations. Currently, there is a tendency in making use of social media during emergency management and response. Most of this effort is performed by a huge number of volunteers browsing through social media data and preparing maps that can be used by professional first responders. Automatic analysis approaches are needed to directly support the response teams in monitoring and also understanding the evolution of facts in social media during an emergency situation. In this paper, we investigate the problem of real-time sub-events identification in social media data (i.e., Twitter, Flickr and YouTube) during emergencies. A processing framework is presented serving to generate situational reports/summaries from social media data. This framework relies in particular on online indexing and online clustering of media data streams. Online indexing aims at tracking the relevant vocabulary to capture the evolution of sub-events over time. Online clustering, on the other hand, is used to detect and update the set of sub-events using the indices built during online indexing. To evaluate the framework, social media data related to Hurricane Sandy 2012 was collected and used in a series of experiments. In particular some online indexing methods have been tested against a proposed method to show their suitability. Moreover, the quality of online clustering has been studied using standard clustering indices. Overall the framework provides a great opportunity for supporting emergency responders as demonstrated in real-world emergency exercises.
https://eprints.bournemouth.ac.uk/23557/
Source: Scopus
Online indexing and clustering of social media data for emergency management
Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.
Journal: Neurocomputing
Volume: 172
Pages: 168-179
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.01.084
Abstract:Social media becomes a vital part in our daily communication practice, creating a huge amount of data and covering different real-world situations. Currently, there is a tendency in making use of social media during emergency management and response. Most of this effort is performed by a huge number of volunteers browsing through social media data and preparing maps that can be used by professional first responders. Automatic analysis approaches are needed to directly support the response teams in monitoring and also understanding the evolution of facts in social media during an emergency situation. In this paper, we investigate the problem of real-time sub-events identification in social media data (i.e., Twitter, Flickr and YouTube) during emergencies. A processing framework is presented serving to generate situational reports/summaries from social media data. This framework relies in particular on online indexing and online clustering of media data streams. Online indexing aims at tracking the relevant vocabulary to capture the evolution of sub-events over time. Online clustering, on the other hand, is used to detect and update the set of sub-events using the indices built during online indexing. To evaluate the framework, social media data related to Hurricane Sandy 2012 was collected and used in a series of experiments. In particular some online indexing methods have been tested against a proposed method to show their suitability. Moreover, the quality of online clustering has been studied using standard clustering indices. Overall the framework provides a great opportunity for supporting emergency responders as demonstrated in real-world emergency exercises.
https://eprints.bournemouth.ac.uk/23557/
Source: Scopus
Online indexing and clustering of social media data for emergency management
Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.
Journal: NEUROCOMPUTING
Volume: 172
Pages: 168-179
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.01.084
https://eprints.bournemouth.ac.uk/23557/
Source: Web of Science (Lite)
Online indexing and clustering of social media data for emergency management
Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.
Journal: Neurocomputing
Volume: 172
Pages: 168-179
Publisher: Elsevier
eISSN: 1872-8286
ISSN: 0925-2312
DOI: 10.1016/j.neucom.2015.01.084
Abstract:Social media becomes a vital part in our daily communication practice, creating a huge amount of data and covering different real-world situations. Currently, there is a tendency in making use of social media during emergency management and response. Most of this effort is performed by a huge number of volunteers browsing through social media data and preparing maps that can be used by professional first responders. Automatic analysis approaches are needed to directly support the response teams in monitoring and also understanding the evolution of facts in social media during an emergency situation. In this paper, we investigate the problem of real-time sub-events identification in social media data (i.e., Twitter, Flickr and YouTube) during emergencies. A processing framework is presented serving to generate situational reports/summaries from social media data. This framework relies in particular on online indexing and online clustering of media data streams. Online indexing aims at tracking the relevant vocabulary to capture the evolution of sub-events over time. Online clustering, on the other hand, is used to detect and update the set of sub-events using the indices built during online indexing. To evaluate the framework, social media data related to Hurricane Sandy 2012 was collected and used in a series of experiments. In particular some online indexing methods have been tested against a proposed method to show their suitability. Moreover, the quality of online clustering has been studied using standard clustering indices. Overall the framework provides a great opportunity for supporting emergency responders as demonstrated in real-world emergency exercises.
https://eprints.bournemouth.ac.uk/23557/
Source: Manual
Preferred by: Hamid Bouchachia
Online indexing and clustering of social media data for emergency management
Authors: Pohl, D., Bouchachia, A. and Hellwagner, H.
Journal: Neurocomputing
ISSN: 0925-2312
Abstract:Social media becomes a vital part in our daily communication practice, creating a huge amount of data and covering different real-world situations. Currently, there is a tendency in making use of social media during emergency management and response. Most of this effort is performed by a huge number of volunteers browsing through social media data and preparing maps that can be used by professional first responders. Automatic analysis approaches are needed to directly support the response teams in monitoring and also understanding the evolution of facts in social media during an emergency situation. In this paper, we investigate the problem of real-time sub-events identification in social media data (i.e., Twitter, Flickr and YouTube) during emergencies. A processing framework is presented serving to generate situational reports/summaries from social media data. This framework relies in particular on online indexing and online clustering of media data streams. Online indexing aims at tracking the relevant vocabulary to capture the evolution of sub-events over time. Online clustering, on the other hand, is used to detect and update the set of sub-events using the indices built during online indexing. To evaluate the framework, social media data related to Hurricane Sandy 2012 was collected and used in a series of experiments. In particular some online indexing methods have been tested against a proposed method to show their suitability. Moreover, the quality of online clustering has been studied using standard clustering indices. Overall the framework provides a great opportunity for supporting emergency responders as demonstrated in real-world emergency exercises.
https://eprints.bournemouth.ac.uk/23557/
Source: BURO EPrints