|
Records |
Links |
|
Author  |
Ahmed Nagy; Jeannie Stamberger |

|
|
Title |
Crowd sentiment detection during disasters and crises |
Type |
Conference Article |
|
Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
|
|
Volume |
|
Issue |
|
Pages |
|
|
|
Keywords |
Bayesian networks; Emergency services; Information systems; Risk management; Social networking (online); Crisis management; Disaster response; Emergency management; Short message; Twitter; Disasters |
|
|
Abstract |
Microblogs are an opportunity for scavenging critical information such as sentiments. This information can be used to detect rapidly the sentiment of the crowd towards crises or disasters. It can be used as an effective tool to inform humanitarian efforts, and improve the ways in which informative messages are crafted for the crowd regarding an event. Unique characteristics of microblogs (lack of context, use of jargon etc) in Tweets expressed by a message-sharing social network during a disaster response require special handling to identify sentiment. We present a systematic evaluation of approaches to accurately and precisely identify sentiment in these Tweets. This paper describes sentiment detection expressed in 3698 Tweets, collected during the September 2010, San Bruno, California gas explosion and resulting fires. The data collected was manually coded to benchmark our techniques. We start by using a library of words with annotated sentiment, SentiWordNet 3.0, to detect the basic sentiment of each Tweet. We complemented that technique by adding a comprehensive list of emoticons, a sentiment based dictionary and a list of out-of-vocabulary words that are popular in brief, online text communications such as lol, wow, etc. Our technique performed 27% better than Bayesian Networks alone, and the combination of Bayesian networks with annotated lists provided marginal improvements in sentiment detection than various combinations of lists. © 2012 ISCRAM. |
|
|
Address |
Carnegie Mellon Silicon Valley, IMT Lucca Institute of Advanced Studies, United States; Disaster Management Initiative, Carnegie Mellon Silicon Valley, United States |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
|
|
|
Track |
Social Media and Collaborative Systems |
Expedition |
|
Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
173 |
|
Share this record to Facebook |
|
|
|
|
Author  |
Ahmed Nagy; Lusine Mkrtchyan; Klaas Van Der Meer |

|
|
Title |
A CBRN detection framework using fuzzy logic |
Type |
Conference Article |
|
Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
|
|
Volume |
|
Issue |
|
Pages |
266-271 |
|
|
Keywords |
Data mining; Decision support systems; Disaster prevention; Fuzzy set theory; Information systems; Decision supports; Degree of credibility; Disaster management; Distributed approaches; Evaluation approach; Human activities; Ordered weighted aggregations; Potential values; Fuzzy logic |
|
|
Abstract |
Identifying a chemical, biological, radiological, and nuclear incident (CBRN) is a challenge. Evidence and health symptoms resulting from CBRN malevolent incident overlap with other normal non malevolent human activities. However, proper fusion of symptoms and evidence can aid in drawing conclusions with a certain degree of credibility about the existence of an incident. There are two types of incidents directly observable, overt, or indirectly observable, covert, which can be detected from the symptoms and consequences. This paper describes a framework for identifying a CBRN incident from available evidence using a fuzzy belief degree distributed approach. We present two approaches for evidence fusion and aggregation; the first, two level cumulative belief degree (CBD) while the second is ordered weighted aggregation of belief degrees (OWA). The evaluation approach undertaken shows the potential value of the two techniques. |
|
|
Address |
SCK/CEN, Belgium |
|
|
Corporate Author |
|
Thesis |
|
|
|
Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
|
|
Language |
English |
Summary Language |
English |
Original Title |
|
|
|
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
|
Series Volume |
|
Series Issue |
|
Edition |
|
|
|
ISSN |
2411-3387 |
ISBN |
9783923704804 |
Medium |
|
|
|
Track |
Decision Support Systems |
Expedition |
|
Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
804 |
|
Share this record to Facebook |