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Author |
Geneviève Dubé; Chelsea Kramer; François Vachon; Sébastien Tremblay |
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Title |
Measuring the impact of a collaborative planning support system on crisis 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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Pages |
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Keywords |
Information systems; Maps; Planning; Collaborative planning; Crisis management; Cwss; Functional simulations; Microworld; Objective assessment; Preliminary analysis; Team cognition; Human resource management |
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Abstract |
Crisis management (CM) is an aspect of command and control characterized by complexity, uncertainty, and severe time pressure. To address these challenges, CM teams can use collaborative work support systems (CWSS) to help plan their intervention and coordination activities. However, the use of CWSS is not necessarily beneficial and in some cases, can impede more than augment performance. Hence, it is essential to understand the impact of a CWSS on team performance and CM teamwork. We have developed a methodology to assess the effectiveness of CWSS by comparing the use of an interactive Smartboard with that of a traditional topographic map during team planning activities. To do so, a dynamic CM situation is simulated using a forest firefighting functional simulation – the C3Fire microworld. We compared two groups of participants on the basis of performance, communication, coordination efficiency, and planning quality. Based on a preliminary analysis, in comparison to maps, the use of a CWSS seems to be beneficial to planning activities and CM coordination. At this point the main contribution of the current on-going project is to provide a method and metrics for the objective assessment of new technology in the context of CM. |
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Address |
École de Psychologie, Université Laval, QC, Canada; CAE Professional Services, Ottawa, Canada |
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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 Volume |
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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 |
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Approved |
no |
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Call Number |
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Serial |
459 |
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Author |
Gerhard Backfried; Christian Schmidt; Gerald Quirchmayr |
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Title |
Cross-Media Linking in Times of Disaster |
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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Keywords |
Crisis communication; data collection; natural disasters; Situational Awareness; social media |
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Abstract |
Many possible links and connections can be observed between the different types of media used for communication during a crisis. These links can be detected and assembled to provide a more complete picture of events. They can be categorized according to the type of destination which yields important information for the gathering process as well as concerning general patterns of how platforms are connected. Tweets, posts and comments thus become parts of larger, linked sets of documents forming compound-documents. These documents stretch across media borders and platforms and provide context and broader information for individual entries. In the current paper we describe some of the links and linking behavior encountered during the floods in Central Europe of 2013 from the perspective of Twitter and Facebook. |
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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 |
1242 |
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Author |
Grégoire Burel; Harith Alani |
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Title |
Crisis Event Extraction Service (CREES) – Automatic Detection and Classification of Crisis-related Content on Social Media |
Type |
Conference Article |
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Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Volume |
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Issue |
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Pages |
597-608 |
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Keywords |
Event Detection, Word Embeddings, Deep Learning, Convolutional Neural Networks, API |
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Abstract |
Social media posts tend to provide valuable reports during crises. However, this information can be hidden in large amounts of unrelated documents. Providing tools that automatically identify relevant posts, event types (e.g., hurricane, floods, etc.) and information categories (e.g., reports on affected individuals, donations and volunteering, etc.) in social media posts is vital for their efficient handling and consumption. We introduce the Crisis Event Extraction Service (CREES), an open-source web API that automatically classifies posts during crisis situations. The API provides annotations for crisis-related documents, event types and information categories through an easily deployable and accessible web API that can be integrated into multiple platform and tools. The annotation service is backed by Convolutional Neural Networks (CNNs) and validated against traditional machine learning models. Results show that the CNN-based API results can be relied upon when dealing with specific crises with the benefits associated with the usage word embeddings. |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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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 |
978-0-692-12760-5 |
Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
ISCRAM 2018 Conference Proceedings - 15th International 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 |
2134 |
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Author |
Guillermo Romera Rodriguez |
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Title |
Parler, Capitol Riots, Alt-Right and Radicalization in Social Media |
Type |
Conference Article |
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Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
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Volume |
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Issue |
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Pages |
268-277 |
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Keywords |
Social Media; Parler; Sentiment Analysis; Alt-Right |
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Abstract |
Social media platforms have risen in popularity since their inception. These platforms have since then come to be at the forefront of controversies, from being accused of election interference to, more recently, disseminating fake news and campaigns to sway political behavior. One such episode took place on January 6 when a group of individuals stormed the United States Capitol, and the social media platform Parler came under scrutiny. The platform was accused of being a place for right-wing extremists and Trump supporters who claimed the 2020 election was fraudulent. Initial reports suggested these individuals used Parler to organize and call others to action. This paper explores the feasibility of using social media to detect alt-right radicalization and examines its possible relation to the Capitol Insurrection and Parler. Moreover, we examine if those events could have been detected and averted through the investigation of the platform. |
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Address |
Pennsylvania State University; Pennsylvania State University; Pennsylvania State University |
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Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
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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 |
978-0-473-66845-7 |
Medium |
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Track |
Social Media for Disaster Response |
Expedition |
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Conference |
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Notes |
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Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2500 |
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Author |
Guoqin Ma; Chittayong Surakitbanharn |
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Title |
Predicting Hurricane Damage Using Social Media Posts Coupled with Physical and Socio-Economic Variables |
Type |
Conference Article |
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Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
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Volume |
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Issue |
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Pages |
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Keywords |
Social media, disaster management, damage prediction |
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Abstract |
During a natural disaster or emergency event, individual social media posts or hot spots may not necessarily correlate
to the most devastated areas. To better understand the correlation between social media and physical damage, we
compare Tweets, data about the physical environment, and socio-economic factors with insurance claim information
(as a proxy for physical damage) from 2017 Hurricane Irma in the state of Florida. We use machine learning
to identify relevant Tweets, sensitivity analyses to identify socio-economic factors, and statistical regression to
determine the predictive capability of insurance claims as a proxy for damage. We find that Tweets alone result in a
poorly fitted regression model of insurance claims, but the inclusion of physical features (e.g., power outages, wind
level) and socio-economic factors (e.g., population density, education, Internet access) improves the model?s fit.
Such models contribute to the knowledge base that may allow social media to predict damage in real-time. |
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Address |
Stanford University, United States of America |
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Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
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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 |
978-84-09-10498-7 |
Medium |
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Track |
T8- Social Media in Crises and Conflicts |
Expedition |
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Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
1955 |
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Author |
Hafiz Budi Firmansyah; Jesus Cerquides; Jose Luis Fernandez-Marquez |
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Title |
Ensemble Learning for the Classification of Social Media Data in Disaster Response |
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 |
710-718 |
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Keywords |
Ensemble learning; image classification; social media; disaster response |
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Abstract |
Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment. |
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Address |
Citizen Cyberlab, CUI, University of Geneva, Switzerland; Citizen Cyberlab, CUI, University of Geneva, Switzerland; IIIA-CSIC, Barcelona, Spain |
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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 |
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Approved |
no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2450 |
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Author |
Haiyan Hao; Yan Wang |
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Title |
Hurricane Damage Assessment with Multi-, Crowd-Sourced Image Data: A Case Study of Hurricane Irma in the City of Miami |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
825-837 |
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Keywords |
Computer Vision, Damage Assessment, Disaster Management, Insurance Claims, Social Networking Platforms. |
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Abstract |
The massive crowdsourced data generated on social networking platforms (e.g. Twitter and Flickr) provide free, real-time data for damage assessment (DA) even during catastrophes. Recent studies leveraging crowdsourced data for DA mainly focused on analyzing textual formats. Crowdsourced images can provide rich and objective information about damage conditions, however, are rarely researched for DA purposes. The highly-varied content and loosely-defined damage forms make it difficult to process and analyze the crowdsourced images. To address this problem, we propose a data-driven DA method based on multi-, crowd-sourced images, which includes five machine learning classifiers organized in a hierarchical structure. The method is validated with a case study investigating the damage condition of the City of Miami caused by Hurricane Irma. The outcome is then compared with a metric derived from NFIP insurance claims data. The proposed method offers a resource for rapid DA that supplements conventional DA methods. |
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Address |
University of Florida; University of Florida |
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Thesis |
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Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; 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 |
978-1-949373-27-73 |
ISBN |
2411-3459 |
Medium |
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Track |
Social Media for Disaster Response and Resilie |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
hhao@ufl.edu |
Approved |
no |
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Call Number |
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Serial |
2274 |
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Author |
Hannah Van Wyk; Kate Starbird |
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Title |
Analyzing Social Media Data to Understand How Disaster-Affected Individuals Adapt to Disaster-Related Telecommunications Disruptions |
Type |
Conference Article |
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Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
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Volume |
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Issue |
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Pages |
704-717 |
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Keywords |
Telecommunications, Adaptations, Social Media, Cellular Phone Service, Wi-Fi Access. |
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Abstract |
Information is a critical need during disasters such as hurricanes. Increasingly, people are relying upon cellular and internet-based technology to communicate that information--modalities that are acutely vulnerable to the disruptions to telecommunication infrastructure that are common during disasters. Focusing on Hurricane Maria (2017) and its long-term impacts on Puerto Rico, this research examines how people affected by severe and sustained disruptions to telecommunications services adapt to those disruptions. Leveraging social media trace data as a window into the real-time activities of people who were actively adapting, we use a primarily qualitative approach to identify and characterize how people changed their telecommunications practices and routines--and especially how they changed their locations--to access Wi-Fi and cellular service in the weeks and months after the hurricane. These findings have implications for researchers seeking to better understand human responses to disasters and responders seeking to identify strategies to support affected populations. |
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Address |
University of Washington; University of Washington |
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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 |
Amanda Hughes; Fiona McNeill; 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 |
978-1-949373-27-64 |
ISBN |
2411-3450 |
Medium |
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Track |
Social Media for Disaster Response and Resilie |
Expedition |
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Conference |
17th International Conference on Information Systems for Crisis Response and Management |
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Notes |
hcvw@uw.edu |
Approved |
no |
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Call Number |
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Serial |
2265 |
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Author |
Hayley Watson; Rachel L. Finn |
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Title |
Social media and the 2013 UK heat wave: Opportunities and challenges for future events |
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 |
757-761 |
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Keywords |
Information systems; Crisis management; Heat waves; Most likely; On-line communities; Preparedness and community; Response; Social media; Working papers; Climatology |
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Abstract |
Studies examining the role of social media (SM) use in a crisis often examine the use of SM following a largescale crisis requiring an immediate response. In contrast, this working paper examines the usefulness of SM during an extended crisis, in the form of a heat wave. Authors use the 2013 UK heat wave as a case study to examine how SM was used by different stakeholders during the event, what function(s) SM had, how it was engaged with by the online community and accordingly, what value it contributed to crisis management activities. Findings show that ultimately the nature of the crisis, particularly in relation to populations who are most likely to be vulnerable to its effects, plays an integral role to the value of SM in preparation and response activities. |
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Address |
Trilateral Research and Consulting, United Kingdom |
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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 |
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Serial |
1069 |
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Author |
Heiko Roßnagel; Jan Zibuschka |
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Title |
Using mobile social media for emergency management – A design science approach |
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 |
Computer simulation; Information systems; Risk management; Crisis management; Design science; Large public events; Mobile social medias; Perceived ease of use; Perceived usefulness; Simulation studies; Social media; Design |
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Abstract |
Over the last couple of years social networks have become very popular and part of our daily lives. With the emergence of powerful smartphones and cheap data rates social media can now be accessed anytime and anywhere. Obviously, it makes sense to also facilitate social media for crisis management and response. In this contribution we present a system design for emergency support based on mobile social media with an emphasis on increasing security during large public events. We follow the design science approach as we provide an artifact design along with a description of its implementation and evaluate our artifact using the simulation study methodology. As a result of this study we gained valuable insight into how the users interact with our system and obtained information on how to improve it. Overall the users were quite satisfied with the perceived usefulness and the perceived ease of use of our system. |
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Address |
Fraunhofer IAO, Germany |
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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 |
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Approved |
no |
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Call Number |
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Serial |
890 |
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Author |
Hemant Purohit; Jennifer Chan |
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Title |
Classifying User Types on Social Media to inform Who-What-Where Coordination during Crisis Response |
Type |
Conference Article |
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Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
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Volume |
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Issue |
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Pages |
656-665 |
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Keywords |
User Classification, Social Media, Crisis Coordination, Organization, Organization-affiliated |
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Abstract |
Timely information is essential for better dynamic situational awareness, which leads to efficient resource planning, coordination, and action. However, given the scale and outreach of social media�a key information sharing platform during crises, diverse types of users participate in discussions during crises, which affect the vetting of information for dynamic situational awareness and response coordination activities. In this paper, we present a user analysis on Twitter during crises for three major user types�Organization, Organizationaffiliated (a person�s self-identifying affiliation with an organization in his/her profile), and Non-affiliated (person not identifying any affiliation), by first classifying users and then presenting their communication patterns during two recent crises. Our analysis shows distinctive patterns of the three user types for participation and communication on social media during crises. Such a user-centric approach to study information sharing during crisis events can act as a precursor to deeper domain-driven content analysis for response agencies. |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Albi, France |
Editor |
Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
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Language |
Englisg |
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 |
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Medium |
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Track |
Social Media Studies |
Expedition |
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Conference |
14th International 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 |
ISCRAM @ idladmin @ |
Serial |
2200 |
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Author |
Hemant Purohit; Shreyansh Bhatt; Andrew Hampton; Valerie Shalin; Amit Sheth; John Flach |
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Title |
With whom to coordinate, why and how in ad-hoc social media communities during crisis response |
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 |
|
Pages |
787-791 |
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Keywords |
Hardware; Ad-hoc community; Crisis response; Influencers; Social media; Virtual responders; Information systems |
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Abstract |
During crises directly affected people and observers join social media communities to discuss the event. They may share information relevant to response coordination, for example, specific resource needs. However, responders face a massive data overload and lack the time to monitor social media traffic for important and trustworthy information. To address these challenges, response teams may attempt manual filtering methods, resulting in limited coverage and quality. Hence, we propose a computational framework for extracting specific resource-related information, and an interface for identifying and engaging with influential participants in the dynamic, evolving social media community. Our approach helps to identify those virtual responders who serve both as sources and disseminators of important information to assist in coordinated emergency response. |
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Address |
Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, 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 |
|
Series Title |
|
Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
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 |
|
|
Call Number |
|
Serial |
858 |
|
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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 |
|
Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
|
|
Volume |
|
Issue |
|
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 |
|
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 |
Hiroko Wilensky |
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Title |
Twitter as a navigator for stranded commuters during the great east Japan earthquake |
Type |
Conference Article |
|
Year |
2014 |
Publication |
ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2014 |
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Volume |
|
Issue |
|
Pages |
697-706 |
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Keywords |
Disasters; Earthquakes; Information systems; Crisis informatics; Disaster situations; Great east japan earthquakes; Railroad systems; Social media; Tokyo metropolitan areas; Twitter; Social networking (online) |
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Abstract |
The increased use of social media, such as Twitter, was widely reported on Japanese media after the Great East Japan Earthquake of March 11, 2011. This study is a qualitative investigation of the use of Twitter by the stranded commuters and their supporters in the Tokyo metropolitan area immediately after the earthquake. This paper describes the possibilities and problems of Twitter use under a rapidly changing disaster situation. During the first evening of this disaster, the Japan Railroad and other railroad systems ceased their operations in the Tokyo area. This left more than five million commuters stranded in the area. Many commuters walked hours to return home, while others struggled to find temporary shelter and stayed overnight in the city. This study also explores if Twitter was an effective navigator for helping stranded commuters return home or find shelter. |
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Address |
University of California, Irvine, 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 |
|
Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
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 |
|
Serial |
1091 |
|
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Author |
Holger Fritze; Christian Kray |
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Title |
Community and Governmental Responses to an Urban Flash Flood |
Type |
Conference Article |
|
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 |
|
Pages |
|
|
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Keywords |
community response; Facebook; information flow; social media; Twitter; urban flash flood |
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Abstract |
In summer of 2014 the city of Münster experienced an urban flash flood not seen before with such intensity in Germany. This paper investigates the subsequent governmental and ad-hoc community response actions with a focus on the chronologies of Facebook and Twitter usage. Interviews identified drawbacks of coordinating volunteers in social media ecosystems. Possible solutions to overcome issues related to the interaction of community and official relief activities are identified. |
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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 |
|
Series Title |
|
Abbreviated Series Title |
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|
|
Series Volume |
|
Series Issue |
|
Edition |
|
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|
ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
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Track |
Social Media Studies |
Expedition |
|
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 |
1231 |
|
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Author |
Hongmin Li; Doina Caragea; Cornelia Caragea |
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Title |
Combining Self-training with Deep Learning for Disaster Tweet Classification |
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 |
719-730 |
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Keywords |
Domain Adaptation, Self-training, Crisis Tweets Classification, BERT, CNN |
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Abstract |
Significant progress has been made towards automated classification of disaster or crisis related tweets using machine learning approaches. Deep learning models, such as Convolutional Neural Networks (CNN), domain adaptation approaches based on self-training, and approaches based on pre-trained language models, such as BERT, have been proposed and used independently for disaster tweet classification. In this paper, we propose to combine self-training with CNN and BERT models, respectively, to improve the performance on the task of identifying crisis related tweets in a target disaster where labeled data is assumed to be unavailable, while unlabeled data is available. We evaluate the resulting self-training models on three crisis tweet collections and find that: 1) the pre-trained language model BERTweet is better than the standard BERT model, when fine-tuned for downstream crisis tweets classification; 2) self-training can help improve the performance of the CNN and BERTweet models for larger unlabeled target datasets, but not for smaller datasets. |
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Address |
Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University; Department of Computer Science, University of Illinois at Chicago |
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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 |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
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Notes |
hongminli@ksu.edu |
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2367 |
|
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Author |
Hongmin Li; Doina Caragea; Cornelia Caragea |
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Title |
Towards Practical Usage of a Domain Adaptation Algorithm in the Early Hours of a Disaster |
Type |
Conference Article |
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Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
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Volume |
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Issue |
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Pages |
692-704 |
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Keywords |
Twitter; Domain adaptation; Disaster; Classification |
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Abstract |
Many machine learning techniques have been proposed to reduce the information overload in social media data during an emergency situation. Among such techniques, domain adaptation approaches present greater potential as compared to supervised algorithms because they don't require labeled data from the current disaster for training. However, the use of domain adaptation approaches in practice is sporadic at best. One reason is that domain adaptation algorithms have parameters that need to be tuned using labeled data from the target disaster, which is presumably not available. To address this limitation, we perform a study on one domain adaptation approach with the goal of understanding how much source data is needed to obtain good performance in a practical situation, and what parameter values of the approach give overall good performance. The results of our study provide useful insights into the practical application of domain adaptation algorithms in real crisis situations. |
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Address |
Kansas State University; University of North Texas |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Albi, France |
Editor |
Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
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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 |
|
Medium |
|
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Track |
Social Media Studies |
Expedition |
|
Conference |
14th International Conference on Information Systems for Crisis Response And Management |
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Notes |
|
Approved |
no |
|
|
Call Number |
ISCRAM @ idladmin @ |
Serial |
2057 |
|
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Author |
Hongmin Li; Nicolais Guevara; Nic Herndon; Doina Caragea; Kishore Neppalli; Cornelia Caragea; Anna Squicciarini; Andrea H. Tapia |
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Title |
Twitter Mining for Disaster Response: A Domain Adaptation Approach |
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 |
|
Issue |
|
Pages |
|
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Keywords |
Disaster Response; domain adaptation; tweet classification |
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Abstract |
Microblogging data such as Twitter data contains valuable information that has the potential to help improve the speed, quality, and efficiency of disaster response. Machine learning can help with this by prioritizing the tweets with respect to various classification criteria. However, supervised learning algorithms require labeled data to learn accurate classifiers. Unfortunately, for a new disaster, labeled tweets are not easily available, while they are usually available for previous disasters. Furthermore, unlabeled tweets from the current disaster are accumulating fast. We study the usefulness of labeled data from a prior source disaster, together with unlabeled data from the current target disaster to learn domain adaptation classifiers for the target. Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data. However, for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior. |
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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 |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
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|
Notes |
|
Approved |
yes |
|
|
Call Number |
|
Serial |
1234 |
|
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|
Author |
Hongmin Li; Xukun Li; Doina Caragea; Cornelia Caragea |
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Title |
Comparison of Word Embeddings and Sentence Encodings for Generalized Representations in Crisis Tweet Classifications |
Type |
Conference Article |
|
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 |
|
Issue |
|
Pages |
480-493 |
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Keywords |
Word Embeddings, Sentence Encodings, Reduced Tweet Representation, Crisis Tweet Classification |
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Abstract |
Many machine learning and natural language processing techniques, including supervised and domain adaptation algorithms, have been proposed and studied in the context of filtering crisis tweets. However, applying these approaches in real-time is still challenging because of time-critical requirements of emergency response operations and also diversities and unique characteristics of emergency events. In this paper, we explore the idea of building “generalized” classifiers for filtering crisis tweets that can be pre-trained, and are thus ready to use in real-time, while generalizing well on future disasters/crises data. We propose to achieve this using simple feature based adaptation with tweet representations based on word embeddings and also sentence-level embeddings, representations which do not rely on unlabeled data to achieve domain adaptations and can be easily implemented. Given that there are different types of word/sentence embeddings that are widely used, we propose to compare them to get a general idea about which type works better with crisis tweets classification tasks. Our experimental results show that GloVe embeddings in general work better with the datasets used in our evaluation, and that the supervised algorithms used in our experiments benefit from GloVe embeddings trained specifically on crisis data. Furthermore, our experimental results show that following GloVe, the sentence embeddings have great potential in crisis tweet tasks. |
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Address |
Kansas State University; Kansas State University; Kansas State University; Kansas State 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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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 |
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ISBN |
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Medium |
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Track |
Social Media and Community Engagement Supporting Resilience Building |
Expedition |
|
Conference |
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Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
1689 |
|
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Author |
Hristo Tanev; Vanni Zavarella; Josef Steinberger |
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Title |
Monitoring disaster impact: detecting micro-events and eyewitness reports in mainstream and social media |
Type |
Conference Article |
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Year |
2017 |
Publication |
Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2017 |
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Volume |
|
Issue |
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Pages |
592-602 |
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Keywords |
Natural language processing; machine learning; crisis computing; disaster effects; social media |
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Abstract |
This paper approaches the problem of monitoring the impact of the disasters by mining web sources for the events, caused by these disasters. We refer to these disaster effects as “micro-events”. Micro-events typically following a large disaster include casualties, damage on infrastructures, vehicles, services and resource supply, as well as relief operations. We present natural language grammar learning algorithms which form the basis for building micro-event detection systems from data, with no or minor human intervention, and we show how they can be applied to mainstream news and social media for monitoring disaster impact. We also experimented with applying statistical classifiers to distill, from social media situational updates on disasters, eyewitness reports from directly affected people. Finally, we describe a Twitter mining robot, which integrates some of these monitoring techniques and is intended to serve as a multilingual content hub for enhancing situational awareness. |
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Address |
European Commission Joint Research Centre; University of West Bohemia |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Albi, France |
Editor |
Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
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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 |
|
Medium |
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Track |
Social Media Studies |
Expedition |
|
Conference |
14th International Conference on Information Systems for Crisis Response And Management |
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Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
2047 |
|
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Author |
Humaira Waqas; Muhammad Imran |
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Title |
#CampFireMissing: An Analysis of Tweets About Missing and Found People From California Wildfires |
Type |
Conference Article |
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Year |
2019 |
Publication |
Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management |
Abbreviated Journal |
Iscram 2019 |
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Volume |
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Issue |
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Pages |
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Keywords |
social media, Twitter, missing and found people, California wildfires, disaster response |
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Abstract |
Several research studies have shown the importance of social media data for humanitarian aid. Among others,
the issue of missing and lost people during disasters and emergencies is crucial for disaster managers. This work
analyzes Twitter data from a recent wildfire event to determine its usefulness for the mitigation of the missing and
found people issue. Data analysis performed using various filtering techniques, and trend analysis revealed that
Twitter contains important information potentially useful for emergency managers and volunteers to tackle this
issue. Many tweets were found containing full names, partial names, location information, and other vital clues
which could be useful for finding missing people. |
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Address |
Qatar Computing Research Institute, Qatar |
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Corporate Author |
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Thesis |
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Publisher |
Iscram |
Place of Publication |
Valencia, Spain |
Editor |
Franco, Z.; González, J.J.; Canós, J.H. |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Series Title |
|
Abbreviated Series Title |
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Series Volume |
|
Series Issue |
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Edition |
|
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ISSN |
2411-3387 |
ISBN |
978-84-09-10498-7 |
Medium |
|
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Track |
T8- Social Media in Crises and Conflicts |
Expedition |
|
Conference |
16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) |
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|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
1915 |
|
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Author |
Hussein Mouzannar; Yara Rizk; Mariette Awad |
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Title |
Damage Identification in Social Media Posts using Multimodal Deep Learning |
Type |
Conference Article |
|
Year |
2018 |
Publication |
ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2018 |
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Volume |
|
Issue |
|
Pages |
529-543 |
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Keywords |
Humanitarian computing, deep neural networks, multimodal learning, natural language processing, visual object recognition. |
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Abstract |
Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared by the minute. Our goal is to automate actionable information extraction from social media posts to efficiently direct relief resources. Identifying damage and human casualties allows first responders to efficiently allocate resources and save as many lives as possible. Since social media posts contain text, images and videos, we propose a multimodal deep learning framework to identify damage related information. This framework combines multiple pretrained unimodal convolutional neural networks that extract features from raw text and images independently, before a final classifier labels the posts based on both modalities. Experiments on a home-grown database of labeled social media posts showed promising results and validated the merits of the proposed approach. |
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Address |
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Corporate Author |
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Thesis |
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Publisher |
Rochester Institute of Technology |
Place of Publication |
Rochester, NY (USA) |
Editor |
Kees Boersma; Brian Tomaszeski |
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|
Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
|
Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
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ISSN |
2411-3387 |
ISBN |
978-0-692-12760-5 |
Medium |
|
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Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management |
|
|
Notes |
|
Approved |
no |
|
|
Call Number |
|
Serial |
2129 |
|
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|
|
Author |
Imen Bizid; Patrice Boursier; Jacques Morcos; Sami Faiz |
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Title |
A Classification Model for the Identification of Prominent Microblogs Users during a Disaster |
Type |
Conference Article |
|
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 |
|
Issue |
|
Pages |
|
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Keywords |
Disaster Management; Information retrieval; Microblogs |
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Abstract |
Content shared in microblogs during disasters is expressed in various formats and languages. This diversity makes the information retrieval process more complex and computationally infeasible in real time. To address this, we propose a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster. Users who have shared at least one tweet about the disaster are modeled using three kinds of time-sensitive features, including topical, social and geographical features. Then, these users are classified into two classes using a linear Support Vector Machine (SVM) to evaluate them over the extracted features and identify the most prominent ones. The first results using the actual dataset, show that our model has a high accuracy by detecting most of the prominent users. Moreover, we demonstrate that all the proposed features used by our model are indispensable to achieve this high accuracy. |
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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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English |
Summary Language |
English |
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ISSN |
2411-3387 |
ISBN |
9788271177881 |
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Track |
Social Media Studies |
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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 |
1241 |
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Author |
Irina Temnikova; Carlos Castillo; Sarah Vieweg |
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Title |
EMTerms 1.0: A Terminological Resource for Crisis Tweets |
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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 |
crises; Terminological resource; Twitter |
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Abstract |
We present the first release of EMTerms (Emergency Management Terms), the largest crisis-related terminological resource to date, containing over 7,000 terms used in Twitter to describe various crises. This resource can be used by practitioners to search for relevant messages in Twitter during crises, and by computer scientists to develop new automatic methods for crises in Twitter.
The terms have been collected from a seed set of terms manually annotated by a linguist and an emergency manager from tweets broadcast during 4 crisis events. A Conditional Random Fields (CRF) method was then applied to tweets from 35 crisis events, in order to expand the set of terms while overcoming the difficulty of getting more emergency managers? annotations.
The terms are classified into 23 information-specific categories, by using a combination of expert annotations and crowdsourcing. This article presents the detailed terminology extraction methodology, as well as final results. |
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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 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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Approved |
yes |
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Call Number |
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Serial |
1229 |
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Author |
Jacqueline Floch; Michael Angermann; Edel Jennings; Mark Roddy |
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Title |
Exploring cooperating smart spaces for efficient collaboration in disaster management |
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 |
Disaster prevention; Disasters; Information management; Information systems; Space platforms; Ubiquitous computing; Crowd participation; Cscw; Current practices; Disaster management; Disaster scenario; Disaster zones; Social computing; User evaluations; Human resource management |
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Abstract |
This paper discusses the applicability of Cooperating Smart Spaces in the disaster management realm and their potential to increase the efficiency and effectiveness of rescue relief teams. The Cooperating Smart Space is a novel concept that combines and extends pervasive computing and social computing to support smart space management and community collaboration. Based on an analysis of current practice, we illustrate how the concept can be exploited in the assessment of a disaster scenario in order to improve information management, collaboration between expert teams and cooperation with online volunteers outside of the disaster zone. We present the results of an initial user evaluation by disaster management experts and conclude with important implications for the design of a Cooperating Smart Space platform. © 2012 ISCRAM. |
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Address |
SINTEF, ICT, Norway; German Aerospace Center (DLR), Germany; Waterford Institute of Technology (TSSG), Ireland |
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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 |
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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 |
108 |
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