Abstract Announcement for International Journal of Information Systems for
Crisis Response and Management (IJISCRAM) 8(3)The contents of the latest
issue of:
*International Journal of Information Systems for Crisis Response and
Management (IJISCRAM)*
*An Official Publication of the ISCRAM Association
<http://www.iscram.org/category-membership/>*
Volume 8, Issue 3, July - September 2016
Indexed by: INSPEC
Published: Quarterly in Print and Electronically
ISSN: 1937-9390; EISSN: 1937-9420;
Published by IGI Global Publishing, Hershey, USA
www.igi-global.com/ijiscram
<http://www.igi-global.com/journal/international-journal-information-systems-crisis/1119>

Editor-in-Chief: Víctor Amadeo Bañuls Silvera (Universidad Pablo de
Olavide, Spain) and Murray E. Jennex (San Diego State University, USA)
*Note: The International Journal of Information Systems for Crisis Response
and Management (IJISCRAM) has an Open Access option, which allows
individuals and institutions unrestricted access to its published content.
Unlike traditional subscription-based publishing models, open access
content is available without having to purchase or subscribe to the journal
in which the content is published. All IGI Global manuscripts are accepted
based on a double-blind peer review editorial process.*

*GUEST EDITORIAL PREFACE*

Special Issue on Contextual Data for Crisis Management and Response

Andrea H. Tapia (School of IS and Technology, Penn State University,
University Park, PA, USA), Kathleen A. Moore (Mercyhurst University, Erie,
PA, USA)

To obtain a copy of the Guest Editorial Preface, click on the link below.
www.igi-global.com/pdf.aspx?tid=180300&ptid=131788&ctid=15&t=Special Issue
on Contextual Data for Crisis Management and Response
<http://www.igi-global.com/pdf.aspx?tid=180300&ptid=131788&ctid=15&t=Special%20Issue%20on%20Contextual%20Data%20for%20Crisis%20Management%20and%20Response>

*ARTICLE 1*

Enabling Rapid Classification of Social Media Communications During Crises

Muhammad Imran (Qatar Computing Research Institute, Doha, Qatar), Prasenjit
Mitra (The Pennsylvania State University, University Park, PA, USA),
Jaideep Srivastava (Qatar Computing Research Institute, Doha, Qatar)

The use of social media platforms such as Twitter by affected people during
crises is considered a vital source of information for crisis response.
However, rapid crisis response requires real-time analysis of online
information. When a disaster happens, among other data processing
techniques, supervised machine learning can help classify online
information in real-time. However, scarcity of labeled data causes poor
performance in machine training. Often labeled data from past event is
available. Can past labeled data be reused to train classifiers? We study
the usefulness of labeled data of past events. We observe the performance
of our classifiers trained using different combinations of training sets
obtained from past disasters. Moreover, we propose two approaches (target
labeling and active learning) to boost classification performance of a
learning scheme. We perform extensive experimentation on real crisis
datasets and show the utility of past-labeled data to train machine
learning classifiers to process sudden-onset crisis-related data in
real-time.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/enabling-rapid-classification-of-social-media-communications-during-crises/180301

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=180301

*ARTICLE 2*

Improving the Utility of Social Media Data to Emergency Responders through
Emotional Content Detection

Shane Halse (Pennsylvania State University, University Park, PA, USA),
Andrea H Tapia (Pennsylvania State University, University Park, PA, USA)

In the following paper, we will present an alternate method for the
detection of emotional content within social media data. Current research
has presented the traditional bag-of-words method in which a predefined
corpus is used to measure the emotional context of each word within a
message. Here we present a method in which a small subset of the data is
labeled to generate a corpus which is then used to detect emotional content
within the data. This research is being conducted on the dataset from
hurricane Sandy in 2012. Our findings show an improvement upon the
bag-of-words method. These findings would further the current research in
improving the utilization of social media data within crisis response. In
doing this we allow the average citizen to provide beneficial data to those
in decision making roles.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/improving-the-utility-of-social-media-data-to-emergency-responders-through-emotional-content-detection/180302

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=180302

*ARTICLE 3*

Predicting Tweet Retweetability during Hurricane Disasters

Venkata Kishore Neppalli (University of North Texas, Computer Science and
Engineering, Denton, TX, USA), Cornelia Caragea (University of North Texas,
Computer Science and Engineering, Denton, TX, USA), Doina Caragea (Kansas
State University, Department of Computer Science, Manhattan, KS, USA),
Murilo Cerqueira Medeiros (University of North Texas, Computer Science and
Engineering, Denton, TX, USA), Andrea H Tapia (Pennsylvania State
University, University Park, PA, USA), Shane E. Halse (Pennsylvania State
University, University Park, PA, USA)

Twitter is a vital source for obtaining information, especially during
events such as natural disasters. Users can spread information on Twitter
either by crafting new posts, which are called “tweets,” or by using the
retweet mechanism to re-post previously created tweets. During natural
disasters, identifying how likely a tweet is to be retweeted is crucial
since it can help promote the spread of useful information in a social
network such as Twitter, as well as it can help stop the spread of
misinformation when corroborated with approaches that identify rumors and
misinformation. In this paper, we present an analysis of retweeted tweets
from two different hurricane disasters, to identify factors that affect
retweetability. We then use these factors to extract features from tweets'
content and user account information in order to develop models that
automatically predict the retweetability of a tweet. The results of our
experiments on Sandy and Patricia Hurricanes show the effectiveness of our
features.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/predicting-tweet-retweetability-during-hurricane-disasters/180303

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=180303

*ARTICLE 4*

Mapping of Areas Presenting Specific Risks to Firefighters Due to Buried
Technical Networks

Amélie Grangeat (French Alternative Energies and Atomic Energy Commission
(CEA), Gramat, France), Stéphane Raclot (Brigade de Sapeurs Pompiers de
Paris (BSPP), Paris, France), Floriane Brill (Brigade de Sapeurs Pompiers
de Paris (BSPP), Paris, France), Emmanuel Lapebie (French Alternative
Energies and Atomic Energy Commission (CEA), Gramat, France)

Vehicles or freight cars on fire below a bridge or inside a tunnel are
exceptional events and imply difficult intervention conditions for
firefighters. A buried technical network like high voltage electricity
line, gas or steam pipeline around such a fire causes additional specifics
risks. Vulnerability areas for firefighters are zones where both factors
exist: a difficult incident area together with a specific risk like buried
networks. They require intervention teams with specific emergency response
capabilities. The paper proposes a method developed for the Paris Fire
Brigade for vulnerability mapping. Results aim at improving the
mobilization in allocating directly the specific responses capabilities
intervention teams. Results are debated from an operational point of view.
Cutting off several network lines during firefighters' interventions may
strongly affect the society. In case of simultaneous incidents in
vulnerable areas, firefighters could be an early warning system and inform
authorities of the risk of services disruption.

To obtain a copy of the entire article, click on the link below.
www.igi-global.com/article/mapping-of-areas-presenting-specific-risks-to-firefighters-due-to-buried-technical-networks/180304

To read a PDF sample of this article, click on the link below.
www.igi-global.com/viewtitlesample.aspx?id=180304

------------------------------
For full copies of the above articles, check for this issue of the
*International
Journal of Information Systems for Crisis Response and Management
(IJISCRAM)* in your institution's library. This journal is also included in
the IGI Global aggregated *"InfoSci-Journals"* database:
www.igi-global.com/isj
<http://www.igi-global.com/e-resources/infosci-databases/infosci-journals/>.
------------------------------

*CALL FOR PAPERS*

Mission of IJISCRAM:

The mission of the *International Journal of Information Systems for Crisis
Response and Management (IJISCRAM)* is to provide an outlet for innovative
research in the area of information systems for crisis response and
management. Research is expected to be rigorous but can utilize any
accepted methodology and may be qualitative or quantitative in nature. The
journal will provide a comprehensive cross disciplinary forum for advancing
the understanding of the organizational, technical, human, and cognitive
issues associated with the use of information systems in responding and
managing crises of all kinds. The goal of the journal is to publish high
quality empirical and theoretical research covering all aspects of
information systems for crisis response and management. Full-length
research manuscripts, insightful research and practice notes, and case
studies will be considered for publication.

Indices of IJISCRAM:

   - ACM Digital Library
   - Bacon's Media Directory
   - Cabell's Directories
   - DBLP
   - GetCited
   - Google Scholar
   - INSPEC
   - JournalTOCs
   - MediaFinder
   - Norwegian Social Science Data Services (NSD)
   - The Index of Information Systems Journals
   - The Standard Periodical Directory
   - Ulrich's Periodicals Directory

Coverage of IJISCRAM:

This journal covers all aspects of the crisis management information
systems discipline, from organizational or social issues to technology
support to decision making and knowledge representation. High quality
submissions are encouraged using any qualitative or quantitative research
methodology, focusing on the design, development, implementation, uses and
evaluation of such systems. Submissions are especially encouraged covering
the following topics in this discipline:

- Case studies, research methods, and modeling approaches
- Collaborative and intelligent systems
- Command and control
- Communication technologies
- Crisis planning, training, exercising, and gaming
- Data fusion, representation, and visualization
- Decision making and judgment
- Disaster risk reduction, risk management, ad-hoc, and sensor networks
- Early warning systems
- Emergency response systems
- Geographical information systems
- Globalization and development issues
- Healthcare and health information systems
- Human-computer interaction
- Humanitarian operations
- Information systems strategy
- Knowledge management and systems
- Systems interoperability information systems infrastructures
- Virtual teams and organizations

Interested authors should consult the journal's manuscript submission
guidelines
www.igi-global.com/calls-for-papers/international-journal-information-systems-crisis/1119
_______________________________________________
AISWorld mailing list
AISWorld@lists.aisnet.org

Betreff:	[AISWorld] CFP IJISCRAM 8(3)
Datum:	Fri, 5 May 2017 00:10:05 -0700
Von:	Murray Jennex <mjennex@mail.sdsu.edu>
An:	aisworld@lists.aisnet.org