| Records |
| Author |
Babajide Osatuyi; Michael J. Chumer |
| Title |
An empirical investigation of alert notifications: A temporal analysis approach |
Type |
Conference Article |
| Year |
2010 |
Publication |
ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings |
Abbreviated Journal |
ISCRAM 2010 |
| Volume |
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Issue |
|
Pages |
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| Keywords |
Social networking (online); Time series analysis; Alert notification; Emergency responders; Empirical investigation; Information and Communication Technologies; Intelligence gathering; Situational awareness; Social Network Sites; Temporal analysis; Information systems |
| Abstract |
As the deployment of situational awareness mechanisms such as geothermal sensors, use of social network sites, and information and communication technologies (e.g., cell phones) become increasingly widespread to emergency responders, the problem of alert analysis has become very important. Broadcast of large amounts of alerts sent back to command centers for processing may impair the ability of analysts to connect dots that may otherwise adequately enable them to make informed decisions in a timely fashion. This paper investigates trends and patterns embedded in alert notifications generated over a given period of time in order to uncover correlations that may exist in the data. Data for this study are obtained from the National Center for Crisis and Continuity Coordination (NC4). We employ classical time series analysis to understand, explain and predict trends and patterns in the data. This work presents results obtained thus far in the quest for the effect of passage of time on alert patterns. Implications of this work in practice and research are discussed. |
| Address |
New Jersey Institute of Technology, United States |
| Corporate Author |
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Thesis |
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| Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Seattle, WA |
Editor |
S. French, B. Tomaszewski, C. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
|
Edition |
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| ISSN |
2411-3387 |
ISBN |
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Medium |
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| Track |
Open Track |
Expedition |
|
Conference |
7th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
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Approved |
no |
| Call Number |
|
Serial |
817 |
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| Author |
Yongzhong Sha; Jinsong Yan; Guoray Cai |
| Title |
Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog |
Type |
Conference Article |
| Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
| Volume |
|
Issue |
|
Pages |
722-726 |
| Keywords |
Air pollution; Information systems; Time series analysis; Crisis; Pm2.5; Public opinions; Sentiment analysis; Social media analysis; Social aspects |
| Abstract |
Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorithm together with a special sentiment words dictionary tailored and refined for Chinese language. The results of time series analysis on detected sentiment strength showed that less than one percent of the posts are strong-positive or strong negative. Weekly sentiment strength measures show symmetric changes in positive and negative strength, but overall trend moved towards more positive opinions. Special attention was given to sharp bursts of sentiment strength that coincide temporally with the occurrence of extreme social events. These findings suggest that sentiment strength analysis may generate useful alert and awareness of pending extreme social events. |
| Address |
Lanzhou University, Gansu, China; Penn State University, University Park, PA, United States |
| Corporate Author |
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Thesis |
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| Publisher |
The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
9780692211946 |
Medium |
|
| Track |
Social Media in Crisis Response and Management |
Expedition |
|
Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
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Approved |
no |
| Call Number |
|
Serial |
939 |
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| Author |
Christopher W. Zobel; Stanley E. Griffis; Steven A. Melnyk; John R. MacDonald |
| Title |
Characterizing disaster resistance and recoveryusing outlier detection |
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 |
Computer simulation; Information systems; Statistics; Time series analysis; Disaster resiliences; Disaster resistance; Interaction effect; Outlier Detection; Predicted Resilience; Resilient behavior; Simulation; Transient behavior; Disasters |
| Abstract |
Most definitions of disaster resilience incorporate both the capacity to resist the initial impact of a disaster and the ability to recover after it occurs. Being able to characterize and analyze resilient behavior can lead to improved understanding not only of the capabilities of a given system, but also of the effectiveness of different strategies for improving its resiliency. This paper presents an approach for quantifying the transient behavior resulting from a disaster event in a way that allows researchers to not only describe the transient response but also assess the impact of various factors (both main and interaction effects) on this response. This new approach combines simulation modeling, time series analysis, and statistical outlier detection to differentiate between disaster resistance and disaster recovery. Following the introduction of the approach, the paper provides a preliminary look at its relationship to the existing concept of predicted disaster resilience. © 2012 ISCRAM. |
| Address |
Virginia Tech, United States; Michigan State University, United States |
| Corporate Author |
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Thesis |
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| Publisher |
Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
9780864913326 |
Medium |
|
| Track |
Analytical Modelling and Simulation |
Expedition |
|
Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
no |
| Call Number |
|
Serial |
247 |
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