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Author |
Pragna Debnath; Saniul Haque; Somprakash Bandyopadhyay; Siuli Roy |
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Title |
Post-disaster Situational Analysis from WhatsApp Group Chats of Emergency Response Providers |
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Conference Article |
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Year |
2016 |
Publication |
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2016 |
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Use of social media has established itself as one of the important information carriers in the field of disaster management. However, use of Twitter and Facebook by victims, first responders and others generates information that is varied, unstructured and unreliable. On the other hand, NGOs, operating in the disaster area, are often involved in intra-organizational communication using messaging apps like WhatsApp, and their group interactions can help in gathering meaningful data for situational analysis and need assessment. Our focus is to automate the process of filtering relevant information, query-based clustering of pertinent information from a WhatsApp group conversation of a specific volunteer group, so that situation analysis and need assessment can be done more rapidly. We have evaluated our scheme using WhatsApp chat log of a medical volunteer group in two post-disaster scenarios and concluded that it can provide valuable insights about region-specific resource requirements and allocation for effective decision making. |
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Federal University of Rio de Janeiro |
Place of Publication |
Rio de Janeiro, Brasil |
Editor |
A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
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English |
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English |
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2411-3388 |
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978-84-608-7984-9 |
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Social Media Studies |
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Conference |
13th International Conference on Information Systems for Crisis Response and Management |
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no |
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1393 |
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Author |
Yang Zhang; William Drake; Yuhong Li; Christopher Zobel; Margaret Cowell |
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Title |
Fostering Community Resilience through Adaptive Learning in a Social Media Age: Municipal Twitter Use in New Jersey following Hurricane Sandy |
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Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Keywords |
Adaptive learning; disaster resilience; Hurricane Sandy; social media; Twitter |
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Abstract |
Adaptive learning capacity is a critical component of community resilience that describes the ability of a community to effectively gauge its vulnerability to the external environment and to make appropriate changes to its coping strategies. Traditionally, the relationship between government and community learning was framed within a deterministic paradigm. Learning outcomes were understood to result from the activities of central actors (i.e., government) and flow passively into the community. The emergence of social media is fundamentally changing the ways organizations and individuals collect and share information. Despite its growing acceptance, it remains to be determined how this shift in communication will ultimately affect community adaptive learning, and therefore, community resilience. This paper presents the initial results of a mixed-methods research effort that examined the use of Twitter in local municipalities from Monmouth County, NJ after Hurricane Sandy. Using a conceptual model of organizational learning, we examine the learning outcomes following the Hurricane Sandy experience. |
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University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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English |
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English |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
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Track |
Social Media Studies |
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ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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yes |
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1236 |
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Author |
Adam Flizikowski, Marcin Przybyszewski; Anna Stachowicz; Tomasz Olejniczak; Rafael Renk |
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Title |
Text Analysis Tool TWeet lOcator ? TAT2 |
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Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Keywords |
AIDA; Crisis Management; iSAR+; location of Twitter messages; social media |
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Abstract |
Information about location and geographical coordinates in particular, may be very important during a crisis event, especially for search and rescue operations ? but currently geo-tagged tweets are extremely rare. Improved capabilities of capturing additional location from Twitter (up to 4 times improvement) are crucial for response efforts given a vast amount of messages exchanged during a crisis event. That is why authors have designed a tool (Text Analysis TWeet lOcator ? TAT2) that relies on existing open source text analysis tools with additional services to provide additional hints about people location. Validation process, complementing experimentation and test results, included involvement of end-users (i.e. Public Protection and Disaster Relief services and citizens during a realistic crisis exercise showcase. In addition, the integration of TAT2 with external tools has also been validated. |
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University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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English |
Summary Language |
English |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Approved |
yes |
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Call Number |
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Serial |
1227 |
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Author |
Yongzhong Sha; Jinsong Yan; Guoray Cai |
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Title |
Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog |
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Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Pages |
722-726 |
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Keywords |
Air pollution; Information systems; Time series analysis; Crisis; Pm2.5; Public opinions; Sentiment analysis; Social media analysis; Social aspects |
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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. |
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Lanzhou University, Gansu, China; Penn State University, University Park, PA, United States |
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The Pennsylvania State University |
Place of Publication |
University Park, PA |
Editor |
S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
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English |
Summary Language |
English |
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ISSN |
2411-3387 |
ISBN |
9780692211946 |
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Track |
Social Media in Crisis Response and Management |
Expedition |
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Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
939 |
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Author |
Thomas Papadimos; Nick Pantelidis; Stelios Andreadis; Aristeidis Bozas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris |
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Title |
Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Pages |
623-635 |
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Keywords |
Alert framework; social media; event detection; kernel density estimation; community detection |
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Abstract |
The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019. |
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Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologie |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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English |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
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Track |
Social Media for Crisis Management |
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Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2443 |
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Author |
Muhammad Imran; Shady Elbassuoni; Carlos Castillo; Fernando Díaz; Patrick Meier |
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Title |
Extracting information nuggets from disaster- Related messages in social media |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Pages |
791-801 |
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Keywords |
Artificial intelligence; Data visualization; Disasters; Information retrieval; Information systems; Learning systems; Social networking (online); Emergency responders; Extracting information; Machine learning methods; Situational awareness; Social media; Supervised classification; Twitter; Visualization system; Emergency services |
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Abstract |
Microblogging sites such as Twitter can play a vital role in spreading information during “natural” or man-made disasters. But the volume and velocity of tweets posted during crises today tend to be extremely high, making it hard for disaster-affected communities and professional emergency responders to process the information in a timely manner. Furthermore, posts tend to vary highly in terms of their subjects and usefulness; from messages that are entirely off-topic or personal in nature, to messages containing critical information that augments situational awareness. Finding actionable information can accelerate disaster response and alleviate both property and human losses. In this paper, we describe automatic methods for extracting information from microblog posts. Specifically, we focus on extracting valuable “information nuggets”, brief, self-contained information items relevant to disaster response. Our methods leverage machine learning methods for classifying posts and information extraction. Our results, validated over one large disaster-related dataset, reveal that a careful design can yield an effective system, paving the way for more sophisticated data analysis and visualization systems. |
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University of Trento, Italy; American Univ. of Beirut, Lebanon; QCRI, Qatar; Microsoft Research, Qatar |
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Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
613 |
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Author |
Soudip Roy Chowdhury; Muhammad Imran; Muhammad Rizwan Asghar; Amer-Yahia, S.; Carlos Castillo |
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Title |
Tweet4act: Using incident-specific profiles for classifying crisis-related messages |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Issue |
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Pages |
834-839 |
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Keywords |
Artificial intelligence; Disaster prevention; Classification methods; Crisis informatics; Disaster management; Micro-blogging platforms; Microblogging; Precision and recall; Standard machines; Twitter data-analytics; Information systems |
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Abstract |
We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: Pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods. |
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University of Trento, Italy; Fehler Textmarke Nicht Definiert, University of Trento, Italy; CNRS, LIG, France; QCRI, Doha, Qatar |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
396 |
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Author |
Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer |
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Title |
A fine-grained sentiment analysis approach for detecting crisis related microposts |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
846-851 |
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Keywords |
Artificial intelligence; Information systems; Learning systems; Risk management; Social networking (online); Amount of information; Emergency management; Microposts; Real-time information; Sentiment analysis; Situational awareness; Systematic evaluation; Twitter; Data mining |
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Abstract |
Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness. |
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Address |
Technische Universität Darmstadt, Germany; Universität Mannheim, Germany |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
927 |
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Author |
Kate Starbird; Grace Muzny; Leysia Palen |
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Title |
Learning from the crowd: Collaborative filtering techniques for identifying on-the-ground Twitterers during mass disruptions |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Issue |
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Pages |
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Keywords |
Artificial intelligence; Information systems; Learning systems; Social networking (online); Support vector machines; Crisis informatics; Human computation; Mass disruption; Microblogging; Political protest; Behavioral research |
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Abstract |
Social media tools, including the microblogging platform Twitter, have been appropriated during mass disruption events by those affected as well as the digitally-convergent crowd. Though tweets sent by those local to an event could be a resource both for responders and those affected, most Twitter activity during mass disruption events is generated by the remote crowd. Tweets from the remote crowd can be seen as noise that must be filtered, but another perspective considers crowd activity as a filtering and recommendation mechanism. This paper tests the hypothesis that crowd behavior can serve as a collaborative filter for identifying people tweeting from the ground during a mass disruption event. We test two models for classifying on-the-ground Twitterers, finding that machine learning techniques using a Support Vector Machine with asymmetric soft margins can be effective in identifying those likely to be on the ground during a mass disruption event. © 2012 ISCRAM. |
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University of Colorado, Boulder, United States; University of Washington, Seattle, United States |
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Simon Fraser University |
Place of Publication |
Vancouver, BC |
Editor |
L. Rothkrantz, J. Ristvej, Z.Franco |
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English |
Summary Language |
English |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
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Track |
Social Media and Collaborative Systems |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
208 |
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Author |
Andrea Zielinski; Stuart E. Middleton; Laurissa N. Tokarchuk; Xinyue Wang |
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Title |
Social media text mining and network analysis for decision support in natural crisis management |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
840-845 |
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Keywords |
Arts computing; Decision support systems; Information systems; Software prototyping; Decision supports; Link analysis; Social media; Text mining; Vgi; Web Mining; Data mining |
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Abstract |
A core issue in crisis management is to extract from the mass of incoming information what is important for situational awareness during mass emergencies. Based on a case study we develop a prototypical application, TweetComp1, which is integrated into the decision-support component of a Tsunami early warning system and demonstrates the applicability of our approach. This paper describes four novel approaches using focused twitter crawling, trustworthiness analysis, geo-parsing, and multilingual tweet classification in the context of how they could be used for monitoring crises. The validity of our state-of-the art text mining and network analysis technologies will be verified in different experiments based on a human annotated gold standard corpus. |
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Address |
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany; IT Innovation Centre, University of Southampton, Southampton, United Kingdom; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
1160 |
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Author |
Louis Ngamassi; Thiagarajan Ramakrishnan; Shahedur Rahman |
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Title |
Examining the Role of Social Media in Disaster Management from an Attribution Theory Perspective |
Type |
Conference Article |
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Year |
2016 |
Publication |
ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2016 |
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Keywords |
Attribution Theory; Social Media; Disaster Management; Disaster Management Phases |
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Abstract |
This paper is related to the use of social media for disaster management by humanitarian organizations. The past decade has seen a significant increase in the use of social media to manage humanitarian disasters. It seems, however, that it has still not been used to its full potential. In this paper, we examine the use of social media in disaster management through the lens of Attribution Theory. Attribution Theory posits that people look for the causes of events, especially unexpected and negative events. The two major characteristics of disasters are that they are unexpected and have negative outcomes/impacts. Thus, Attribution Theory may be a good fit for explaining social media adoption patterns by emergency managers. We propose a model, based on Attribution Theory, which is designed to understand the use of social media during the mitigation and preparedness phases of disaster management. We also discuss the theoretical contributions and some practical implications. This study is still in its nascent stage and is research in progress. |
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Publisher |
Federal University of Rio de Janeiro |
Place of Publication |
Rio de Janeiro, Brasil |
Editor |
A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto |
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Language |
English |
Summary Language |
English |
Original Title |
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ISSN |
2411-3388 |
ISBN |
978-84-608-7984-9 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
13th International Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
no |
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Call Number |
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Serial |
1399 |
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Author |
Gaëtan Caillaut; Cécile Gracianne; Nathalie Abadie; Guillaume Touya; Samuel Auclair |
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Title |
Automated Construction of a French Entity Linking Dataset to Geolocate Social Network Posts in the Context of Natural Disasters |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Volume |
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Issue |
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Pages |
654-663 |
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Keywords |
Automated geotagging; French Entity Linking; Wikipedia; Twitter; Crisis Management; Natural Disaster |
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Abstract |
During natural disasters, automatic information extraction from Twitter posts is a valuable way to get a better overview of the field situation. This information has to be geolocated to support effective actions, but for the vast majority of tweets, spatial information has to be extracted from texts content. Despite the remarkable advances of the Natural Language Processing field, this task is still challenging for current state-of-the-art models because they are not necessarily trained on Twitter data and because high quality annotated data are still lacking for low resources languages. This research in progress address this gap describing an analytic pipeline able to automatically extract geolocatable entities from texts and to annotate them by aligning them with the entities present in Wikipedia/Wikidata resources. We present a new dataset for Entity Linking on French texts as preliminary results, and discuss research perspectives for enhancements over current state-of-the-art modeling for this task. |
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Address |
BRGM; BRGM; LASTIG, Univ Gustave Eiffel, IGN-ENSG; LASTIG, Univ Gustave Eiffel, IGN-ENSG; BRGM |
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Corporate Author |
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Thesis |
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Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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Language |
English |
Summary Language |
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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 |
978-82-8427-099-9 |
Medium |
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Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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Notes |
|
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2445 |
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Author |
Francesca Comunello; Simone Mulargia; Piero Polidoro; Emanuele Casarotti; Valentino Lauciani |
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Title |
No Misunderstandings During Earthquakes: Elaborating and Testing a Standardized Tweet Structure for Automatic Earthquake Detection Information |
Type |
Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Volume |
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Issue |
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Pages |
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Keywords |
Automatic detection; earthquakes; tweet comprehension; tweet syntax; Twitter |
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Abstract |
Social media have proven to be useful resources for spreading verified information during natural disasters. Nevertheless, little attention has hitherto been devoted to the peculiarities of constructing effective tweets (and tweet formats), or to common users? comprehension of tweets conveying scientific information. In this paper, social scientists and seismologists collaborated in order to elaborate and test a standardized tweet structure to be used during earthquakes, expanding on the results of a quali-quantitative research project. The tweet format is specifically designed to launch an innovative information service by Istituto Nazionale di Geofisica e Vulcanologia (INGV): tweeting the automatic detection of earthquakes with a magnitude greater than 3. This paper illustrates the steps of the research process that led to elaborating a tweet format that will be used in the next few months by the official Twitter account @INGVterremoti. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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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 |
9788271177881 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
yes |
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Call Number |
|
Serial |
1232 |
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Author |
Murray E. Jennex |
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Title |
Social media – Truly viable for crisis response? |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Volume |
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Issue |
|
Pages |
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Keywords |
Availability; Hardware; Cell phone; Crisis events; Crisis response; San Diego; Social media; Information systems |
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Abstract |
On September 8, 2011 the Great San Diego/Southwest Blackout occurred. Approximately 5 million people were affected by this blackout. This paper explores the availability of social media following such a crisis event. Contrary to expectations, the cell phone system did not have the expected availability and as a result, users had a difficult time using social media to status/contact family and friends. This paper presents a survey exploring the use and availability of social media during the Great San Diego/Southwest Blackout event. © 2012 ISCRAM. |
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Address |
San Diego State University, United States |
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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 |
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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 |
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Track |
Social Media and Collaborative Systems |
Expedition |
|
Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
|
Serial |
23 |
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Author |
Aibek Musaev; De Wang; Calton Pu |
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Title |
LITMUS: Landslide detection by integrating multiple sources |
Type |
Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Volume |
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Issue |
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Pages |
677-686 |
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Keywords |
Bayesian networks; Disasters; Hazards; Information systems; Integration; Landslides; Nasa; Rain; Rain gages; Landslide detection; Litmus; Multi-source integrations; Physical sensors; Social sensors; Data integration |
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Abstract |
Disasters often lead to other kinds of disasters, forming multi-hazards such as landslides, which may be caused by earthquakes, rainfalls, water erosion, among other reasons. Effective detection and management of multihazards cannot rely only on one information source. In this paper, we evaluate a landslide detection system LITMUS, which combines multiple physical sensors and social media to handle the inherent varied origins and composition of multi-hazards. LITMUS integrates near real-time data from USGS seismic network, NASA TRMM rainfall network, Twitter, YouTube, and Instagram. The landslide detection process consists of several stages of social media filtering and integration with physical sensor data, with a final ranking of relevance by integrated signal strength. Applying LITMUS to data collected in October 2013, we analyzed and filtered 34.5k tweets, 2.5k video descriptions and 1.6k image captions containing landslide keywords followed by integration with physical sources based on a Bayesian model strategy. It resulted in detection of all 11 landslides reported by USGS and 31 more landslides unreported by USGS. An illustrative example is provided to demonstrate how LITMUS' functionality can be used to determine landslides related to the recent Typhoon Haiyan. |
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Address |
Georgia Institute of Technology, United States |
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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. |
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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 |
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Track |
Social Media in Crisis Response and Management |
Expedition |
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Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
|
Serial |
801 |
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Author |
Ahmed Nagy; Jeannie Stamberger |
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Title |
Crowd sentiment detection during disasters and crises |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Volume |
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Issue |
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Pages |
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Keywords |
Bayesian networks; Emergency services; Information systems; Risk management; Social networking (online); Crisis management; Disaster response; Emergency management; Short message; Twitter; Disasters |
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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. |
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Address |
Carnegie Mellon Silicon Valley, IMT Lucca Institute of Advanced Studies, United States; Disaster Management Initiative, Carnegie Mellon Silicon Valley, United States |
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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 |
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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 |
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Track |
Social Media and Collaborative Systems |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
|
Serial |
173 |
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Author |
Antone Evans Jr.; Yingyuan Yang; Sunshin Lee |
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Title |
Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses |
Type |
Conference Article |
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Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
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Volume |
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Issue |
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Pages |
792-807 |
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Keywords |
Big Data Analysis, Machine Learning, COVID-19, Twitter |
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Abstract |
During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases. |
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Address |
University of Illinois Springfield; University of Illinois Springfield; University of Illinois Springfield |
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Corporate Author |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
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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 |
978-1-949373-61-5 |
ISBN |
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Medium |
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Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
aevan7@uis.edu |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2374 |
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Author |
Antonin Segault; Federico Tajariol; Ioan Roxin |
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Title |
#geiger : Radiation Monitoring Twitter Bots for Nuclear Post-Accident Situations |
Type |
Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Volume |
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Issue |
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Pages |
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Keywords |
bots; long-term period; nuclear post-accident; radiations; Twitter |
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Abstract |
In the last decade, people have increasingly relied on social media platforms such as Twitter to share information on the response to a natural or a man-made disaster. This paper focuses on the aftermath of the Fukushima Daiichi nuclear disaster. Since the disaster, victims and volunteers have been sharing relevant information about radiation measurements by means of social media. The aim of this research is to explore the diffusion of information produced and shared by Twitter bots, to understand the degree of popularity of these sources and to check if these bots deliver original radiation measurements. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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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 |
9788271177881 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
yes |
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Call Number |
|
Serial |
1239 |
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Author |
Herrera, L.C.; Gjøsæter, T. |
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Title |
Leveraging Crisis Informatics Experts: A co-creating approach for validation of social media research insights |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
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Volume |
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Issue |
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Pages |
439-448 |
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Keywords |
Card Sorting Workshop; Practice-Based Research; Crisis Informatics; Support Information System; Validation. |
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Abstract |
Validation of findings is a challenge in practice-based research. While analysis is being conducted and findings are being constructed out of data collected in a defined period, practitioners continue with their activities. This issue is exacerbated in the field of crisis management, where high volatility and personnel turnover make the capacity to attend research demands scarce. Therefore, conducting classic member validation is logistically challenging for the researcher. The need for rigor and validity calls for alternative mechanisms to fulfill requirements for academic research. This article presents an approach for validation of results of a qualitative study with public organizations that use social media as a source of information in the context of crisis management. The unavailability of original interview-objects to validate our findings resulted in an alternative validation method that leveraged experts in crisis informatics. By presenting our approach, we contribute to encouraging rigor in qualitative research while maintaining the relationship between practice and academia. |
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Address |
University of Agder; University of Agder |
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Corporate Author |
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Thesis |
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Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
Hosssein Baharmand |
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 |
1 |
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ISSN |
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ISBN |
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Medium |
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Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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Notes |
http://dx.doi.org/10.59297/MHCV5804 |
Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2538 |
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Author |
Ryo Otaka; Osamu Uchida; Keisuke Utsu |
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Title |
Prototype of Notification and Status Monitoring System Using LINE Smartphone Application to Support Local Communities |
Type |
Conference Article |
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Year |
2018 |
Publication |
Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. |
Abbreviated Journal |
Iscram Ap 2018 |
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Volume |
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Issue |
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Pages |
450-458 |
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Keywords |
Care, Application, Social media |
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Abstract |
Japanese society is aging rapidly, so an increasing number of households currently consists of only elderly single people or couples. We propose a system that uses LINE (a mobile communication application) for sending notices containing information from local governments to elderly or physically disabled people, as well as for efficient monitoring by local governments and social workers of the health conditions and statuses of such people. Our system can be used by anyone who has a smartphone with LINE installed. We have also conducted an operational test of a prototype of our system. |
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Address |
Tokai University; Tokai University; Tokai University |
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Corporate Author |
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Thesis |
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Publisher |
Massey Univeristy |
Place of Publication |
Albany, Auckland, New Zealand |
Editor |
Kristin Stock; Deborah Bunker |
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Language |
English |
Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Track |
Social Media and Community Engagement Supporting Resilience Building |
Expedition |
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Conference |
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Notes |
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Approved |
no |
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Call Number |
|
Serial |
1659 |
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Author |
Faisal Luqman; Martin Griss |
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Title |
Leveraging mobile context for effective collaboration and task management |
Type |
Conference Article |
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Year |
2011 |
Publication |
8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 |
Abbreviated Journal |
ISCRAM 2011 |
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Volume |
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Issue |
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Pages |
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Keywords |
Carrier mobility; Information systems; Mobile devices; Agent-based systems; Collaboration; Command and control; Context information; Dynamic role-based; Emergent volunteer; Large scale disasters; Multi-agent; Human resource management |
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Abstract |
Collaboration and task management is challenging in distributed, dynamically-formed teams, typical in large scale disaster response scenarios. Ineffective collaboration may potentially result in poor performance and loss of life. The increased adoption of sensor rich mobile devices allow for mobile context to be leveraged. In this paper, we present Overseer, an agent-based system that exploits context information from mobile devices to facilitate collaboration and task allocation. We describe how mobile context can be used to create dynamic role-based assignments to enhance collaboration and effective task management. |
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Address |
Carnegie Mellon Silicon Valley, United States |
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Corporate Author |
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Thesis |
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Publisher |
Information Systems for Crisis Response and Management, ISCRAM |
Place of Publication |
Lisbon |
Editor |
M.A. Santos, L. Sousa, E. Portela |
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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 |
9789724922478 |
Medium |
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Track |
Social Media and Collaborative Systems |
Expedition |
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Conference |
8th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
no |
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Call Number |
|
Serial |
730 |
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Author |
Benjamin Herfort; João Porto De Albuquerque; Svend-Jonas Schelhorn; Alexander Zipf |
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Title |
Does the spatiotemporal distribution of tweets match the spatiotemporal distribution of flood phenomena? A study about the River Elbe Flood in June 2013 |
Type |
Conference Article |
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Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Volume |
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Issue |
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Pages |
747-751 |
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Keywords |
Catchments; Data mining; Information systems; Social networking (online); Spatial distribution; Water levels; Crisis management; Digital elevation model; Geographical features; Situational awareness; Social media; Social media platforms; Spatiotemporal distributions; Twitter; Floods |
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Abstract |
In this paper we present a new approach to enhance information extraction from social media that relies upon the geographical relations between twitter data and flood phenomena. We use specific geographical features like hydrological data and digital elevation models to analyze the spatiotemporal distribution of georeferenced twitter messages. This approach is applied to examine the River Elbe Flood in Germany in June 2013. Although recent research has shown that social media platforms like Twitter can be complementary information sources for achieving situation awareness, previous work is mostly concentrated on the classification and analysis of tweets without resorting to existing data related to the disaster, e.g. catchment borders or sensor data about river levels. Our results show that our approach based on geographical relations can help to manage the high volume and velocity of social media messages and thus can be valuable for both crisis response and preventive flood monitoring. |
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Address |
GIScience Department, Heidelberg University, Germany; Dept. of Computer Systems/ICMC, University of Sao Paulo, Brazil |
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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. |
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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 |
|
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Track |
Social Media in Crisis Response and Management |
Expedition |
|
Conference |
11th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
|
Approved |
no |
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Call Number |
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Serial |
572 |
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Author |
Robert Thomson; Naoya Ito; Hinako Suda; Fangyu Lin; Yafei Liu.; Ryo Hayasaka; Ryuzo Isochi; Zhou Wang |
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Title |
Trusting tweets: The Fukushima disaster and information source credibility on Twitter |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Keywords |
Cell culture; Information systems; Nuclear power plants; Social networking (online); Anonymity; Credibility; Crisis communications; Deindividuation; Fukushima; Social media; Trust; Twitter; Disasters |
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Abstract |
This paper focuses on the micro-blogging service Twitter, looking at source credibility for information shared in relation to the Fukushima Daiichi nuclear power plant disaster in Japan. We look at the sources, credibility, and between-language differences in information shared in the month following the disaster. Messages were categorized by user, location, language, type, and credibility of information source. Tweets with reference to third-party information made up the bulk of messages sent, and it was also found that a majority of those sources were highly credible, including established institutions, traditional media outlets, and highly credible individuals. In general, profile anonymity proved to be correlated with a higher propensity to share information from low credibility sources. However, Japanese-language tweeters, while more likely to have anonymous profiles, referenced low-credibility sources less often than non-Japanese tweeters, suggesting proximity to the disaster mediating the degree of credibility of shared content. © 2012 ISCRAM. |
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Address |
Graduate School of International Media, Communication and Tourism Studies, Japan; Research Faculty of Media Communication, Hokkaido University, Japan |
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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 |
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Language |
English |
Summary Language |
English |
Original Title |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9780864913326 |
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Track |
Social Media and Collaborative Systems |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
216 |
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Author |
Andrea Kavanaugh; Steven D. Sheetz; Riham Hassan; Seungwon Yang; Hicham G. Elmongui; Edward A. Fox; Mohamed Magdy; Donald J. Shoemaker |
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Title |
Between a rock and a cell phone: Communication and information technology use during the 2011 Egyptian uprising |
Type |
Conference Article |
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Year |
2012 |
Publication |
ISCRAM 2012 Conference Proceedings – 9th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2012 |
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Volume |
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Issue |
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Pages |
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Keywords |
Cellular telephones; Information systems; Mobile phones; Contextual factors; Information technology use; Innovation diffusion; Innovation diffusion theory; Middle East; Opinion leaders; Social media; Social media datum; Social networking (online) |
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Abstract |
Many observers heralded the use of social media during recent political uprisings in the Middle East even dubbing Iran's post election protests a “Twitter Revolution”. We seek to put into perspective the use of social media in Egypt during the mass political demonstrations in 2011. We draw on innovation diffusion theory to argue that these media could have had an impact beyond their low adoption rates due to other factors related to demographics and social networks. We supplement our social media data analysis with survey data we collected in June 2011 from an opportunity sample of Egyptian youth. We conclude that in addition to the contextual factors noted above, the individuals within Egypt who used Twitter during the uprising have the characteristics of opinion leaders. These findings contribute to knowledge regarding the role of opinion leaders and social media, especially Twitter, during violent political demonstrations. © 2012 ISCRAM. |
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Address |
Virginia Tech, Blacksburg, VA 24061, United States; Arab Academy for Science and Technology, Cairo, Egypt; Alexandria University, Alexandria, Egypt |
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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 |
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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 |
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Track |
Social Media and Collaborative Systems |
Expedition |
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Conference |
9th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
138 |
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Author |
Rémy Bossu; Robert Steed; Gilles Mazet-Roux; Caroline Etivant; Fréderic Roussel |
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Title |
THE EMSC TOOLS USED TO DETECT AND DIAGNOSE THE IMPACT OF GLOBAL EARTHQUAKES FROM DIRECT AND INDIRECT EYEWITNESSES? CONTRIBUTIONS |
Type |
Conference Article |
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Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
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Pages |
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Keywords |
citizen science; crowdsourcing; flashsourcing; situation awareness; social media |
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Abstract |
This paper presents the strategy and operational tools developed and implemented at the Euro-Mediterranean Seismological Centre (EMSC) to detect and diagnose the impact of global earthquakes within minutes by combining « flashsourcing » (real time monitoring of website traffic) with social media monitoring and crowdsourcing.
This approach serves both the seismological community and the public and can contribute to improved earthquake response. It collects seismological observations, improves situation awareness from a few tens of seconds to a couple of hours after earthquake occurrence and is the basis of innovative targeted real time public information services.
We also show that graphical input methods can improve crowdsourcing tools both for the increasing use of mobile devices and to erase language barriers. Finally we show how social network harvesting could provide information on indirect earthquake effects such as triggered landslides and fires, which are difficult to predict and monitor through existing geophysical networks. |
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Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
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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 |
9788271177881 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
yes |
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Call Number |
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Serial |
1235 |
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