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| Author | Anthony C. Robinson; Alexander Savelyev; Scott Pezanowski; Alan M. MacEachren | ||||
| Title | Understanding the utility of geospatial information in social media | Type | Conference Article | ||
| Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
| Volume | Issue | Pages | 918-922 | ||
| Keywords | Information systems; Job analysis; Visualization; Evaluation; Geo-spatial informations; Geographic information; Geovisual analytics; Situational awareness; Social media; Visual analytics; Visual analytics systems; Information science | ||||
| Abstract | Crisis situations generate tens of millions of social media reports, many of which contain references to geographic features and locations. Contemporary systems are now capable of mining and visualizing these location references in social media reports, but we have yet to develop a deep understanding of what end-users will expect to do with this information when attempting to achieve situational awareness. To explore this problem, we have conducted a utility and usability analysis of SensePlace2, a geovisual analytics tool designed to explore geospatial information found in Tweets. Eight users completed a task analysis and survey study using SensePlace2. Our findings reveal user expectations and key paths for solving usability and utility issues to inform the design of future visual analytics systems that incorporate geographic information from social media. | ||||
| Address | Department of Geography, GeoVISTA Center, Penn State University, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
| Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 883 | |||
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| Author | Rode-Hasinger, S.; Haberle, M.; Racek, D.; Kruspe, A.; Zhu Xiao Xiang | ||||
| Title | TweEvent: A dataset of Twitter messages about events in the Ukraine conflict | Type | Conference Article | ||
| Year | 2023 | Publication | Proceedings of the 20th International ISCRAM Conference | Abbreviated Journal | Iscram 2023 |
| Volume | Issue | Pages | 407-416 | ||
| Keywords | Conflict; Ukraine; Dataset; Social Media; NLP | ||||
| Abstract | Information about incidents within a conflict, e.g., shelling of an area of interest, is scattered amongst different data or media sources. For example, the ACLED dataset continuously documents local incidents recorded within the context of a specific conflict such as Russia’s war in Ukraine. However, these blocks of information might be incomplete. Therefore, it is useful to collect data from several sources to enrich the information pool of a certain incident. In this paper, we present a dataset of social media messages covering the same war events as those collected in the ACLED dataset. The information is extracted from automatically geocoded Twitter text data using state-of-the-art natural language processing methods based on large pre-trained language models (LMs). Our method can be applied to various textual data sources. Both the data as well as the approach can serve to help human analysts obtain a broader understanding of conflict events. | ||||
| Address | Technical University of Munich; Technical University of Munich; Ludwig-Maximilians-Universitat M¨unchen; Technische Hochschule N¨urnberg; Technical University of Munich | ||||
| Corporate Author | Thesis | ||||
| Publisher | University of Nebraska at Omaha | Place of Publication | Omaha, USA | Editor | Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Hosssein Baharmand | Series Title | Abbreviated Series Title | ||
| Series Volume | Series Issue | Edition | 1 | ||
| ISSN | ISBN | Medium | |||
| Track | Social Media for Crisis Management | Expedition | Conference | ||
| Notes | http://dx.doi.org/10.59297/AIDF1102 | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2535 | ||
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| Author | Jakob Rogstadius; Claudio Teixeira; Evangelos Karapanos; Vassilis Kostakos | ||||
| Title | An introduction for system developers to volunteer roles in crisis response and recovery | Type | Conference Article | ||
| Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
| Volume | Issue | Pages | 874-883 | ||
| Keywords | Computer software; Information management; Collaboration; Crisis management; Developer guidelines; Disaster response; System development; Volunteering; Information systems | ||||
| Abstract | Technological advances, such as software tools for citizen reporting, first responder support, and online collaborative information management and mapping, are enabling new or improved forms of volunteering in humanitarian crisis. However, the change is largely driven by the technical community and many proposed solutions are never integrated into community response efforts, indicating mismatches between designs and real world needs. This paper offers readers with a technical background insight into roles, goals and constraints of humanitarian crisis response. In particular, we present three seemingly conflicting views regarding how citizens can contribute to response activities as spontaneous volunteers. With examples from two field studies and grounded in literature review, we integrate the three viewpoints into a framework explaining how the roles of volunteers and trained professionals shift with increasing severity and scale of a crisis. Based on this framework, we also discuss high-level opportunities for supporting crisis response with new software tools. | ||||
| Address | Madeira Interactive Technologies Institute, Portugal; University of Oulu, Finland | ||||
| Corporate Author | Thesis | ||||
| Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
| Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 885 | |||
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| Author | Heiko Roßnagel; Jan Zibuschka | ||||
| Title | Using mobile social media for emergency management – A design science approach | Type | Conference Article | ||
| 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 |
| Volume | Issue | Pages | |||
| 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 | ||||
| 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. | ||||
| Address | Fraunhofer IAO, Germany | ||||
| Corporate Author | Thesis | ||||
| Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Lisbon | Editor | M.A. Santos, L. Sousa, E. Portela |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9789724922478 | Medium | |
| Track | Social Media and Collaborative Systems | Expedition | Conference | 8th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 890 | |||
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| Author | Ryo Otaka; Osamu Uchida; Keisuke Utsu | ||||
| Title | Prototype of Notification and Status Monitoring System Using LINE Smartphone Application to Support Local Communities | 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 |
| Volume | Issue | Pages | 450-458 | ||
| Keywords | Care, Application, Social media | ||||
| 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. | ||||
| Address | Tokai University; Tokai University; Tokai University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Massey Univeristy | Place of Publication | Albany, Auckland, New Zealand | Editor | Kristin Stock; Deborah Bunker |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Track | Social Media and Community Engagement Supporting Resilience Building | Expedition | Conference | ||
| Notes | Approved | no | |||
| Call Number | Serial | 1659 | |||
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| Author | André Sabino; Armanda Rodrigues | ||||
| Title | Understanding the role of cooperation in emergency plan construction | Type | Conference Article | ||
| 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 |
| Volume | Issue | Pages | |||
| Keywords | Computer supported cooperative work; Geographic information systems; Interactive computer systems; Co-operation strategy; Emergency planning; Emergency response plans; Extract informations; Information organization; Information representation; Manage information; Spatial informations; Information systems | ||||
| Abstract | In this paper we describe a proposal for information organization for computer supported cooperative work, while working with spatial information. It is focused on emergency response plan construction, and the requirements extracted from that task. At the centre of our proposal is the analysis of the structure of the cooperative workspace. We argue that the internal information representation should follow a spatial approach, tying the structure used to manage users with the structure used to manage information, suggesting the use of different spaces to represent the information. The gain we expect from this approach is the improved capacity to extract information on how people are cooperating and their relationship with the information they are working with. The ideas are introduced while focusing on real life emergency planning activities, where we discuss the current shortcomings of the cooperation strategies in use and propose a solution. | ||||
| Address | Departamento de Informática, Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2829-516 Caparica, Portugal | ||||
| Corporate Author | Thesis | ||||
| Publisher | Information Systems for Crisis Response and Management, ISCRAM | Place of Publication | Lisbon | Editor | M.A. Santos, L. Sousa, E. Portela |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9789724922478 | Medium | |
| Track | Social Media and Collaborative Systems | Expedition | Conference | 8th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 903 | |||
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| Author | Samuel Lee Toepke | ||||
| Title | Temporal Sampling Implications for Crowd Sourced Population Estimations from Social Media | Type | Conference Article | ||
| Year | 2017 | Publication | Proceedings of the 14th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2017 |
| Volume | Issue | Pages | 564-571 | ||
| Keywords | Population estimation; emergency response; temporal sampling; volunteered geospatial information; data mining | ||||
| Abstract | Understanding the movements of a population throughout the 24-hour day is critical when directing disaster response in an urban area. An emergency situation can develop rapidly, and understanding the expected locations of groups of people is required for the success of first responders. Recent advances in modern consumer technologies have facilitated the generation, sharing and mining of an extensive amount of volunteered geographic information. Users leverage inexpensive smart devices, pervasive Internet connections and social media services to provide data about geospatial locations. Using an enterprise system, it is possible to aggregate this freely available, geospatially enabled data and create a population estimation with high spatiotemporal resolution, via a heat map. This investigation explores the effects of different temporal sampling periods when creating such estimations. Time periods are selected, estimations are generated for several large urban areas in the western United States, and comparisons of the results are shown/discussed. | ||||
| Address | Private Engineering Firm | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Albi, France | Editor | Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | Medium | ||
| Track | Social Media Studies | Expedition | Conference | 14th International Conference on Information Systems for Crisis Response And Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 2044 | |||
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| Author | Samuel Lee Toepke | ||||
| Title | Leveraging Elasticsearch and Botometer to Explore Volunteered Geographic Information | 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 |
| Volume | Issue | Pages | 663-676 | ||
| Keywords | Crisis Management and Response, Elasticsearch, Social Media, Volunteered Geographic Information, Botometer | ||||
| Abstract | In the past year, numerous weather-related disasters have continued to display the critical importance of crisis management and response. Volunteered geographic information (VGI) has been previously shown to provide illumination during all parts of the disaster timeline. Alas, for a geospatial area, the amount of data provided can cause information overload, and be difficult to process/visualize. This work presents a set of open-source tools that can be easily configured, deployed and maintained, to leverage data from Twitter's streaming service. The user interface presents data in near real-time, and allows for dynamic queries, visualizations, maps and dashboards. Another VGI challenge is quantifying trustworthiness of the data. The presented work shows integration of a Twitter-bot assessment service, which uses several heuristics to determine the bot-ness of a Twitter account. Architecture is described, Twitter data from a major metropolitan area is explored using the tools, and conclusions/follow-on work are discussed. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Rochester Institute of Technology | Place of Publication | Rochester, NY (USA) | Editor | Kees Boersma; Brian Tomaszeski |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-0-692-12760-5 | Medium | |
| 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 | 2140 | |||
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| Author | Sandrine Bubendorff; Caroline Rizza | ||||
| Title | The Wikipedia Contribution to Social Resilience During Terrorist Attacks | Type | Conference Article | ||
| Year | 2020 | Publication | ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2020 |
| Volume | Issue | Pages | 790-801 | ||
| Keywords | Wikipedia, Resilience Process, Terrorist Attacks, Social Media. | ||||
| Abstract | This paper aims at studying the role of Wikipedia in social resilience processes during terrorist attacks. It discusses how Wikipedia users' specific skills are mobilized in order to make sense of the event as it unfolds. We have conducted an ethnographic analysis of several Wikipedia's terrorist attacks pages as well as interviews with regular Wikipedia's contributors. We document how Wikipedia is used during crisis by readers and contributors. Doing so, we identify a specific pace of contributions which provides reliable information to readers. By discussing the conditions of their trustworthiness, we highlight how historical sources (i.e. traditional media and authorities) support this pace. Our analyses demonstrate that citizens are engaging very quickly in processes of resilience and should be, therefore, considered as relevant partners by authorities when engaging a response to the crisis. | ||||
| Address | i3-SES, Telecom Paris, IP Paris, CNRS; i3-SES, Telecom Paris, IP Paris, CNRS | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-27-70 | ISBN | 2411-3456 | Medium | |
| Track | Social Media for Disaster Response and Resilie | Expedition | Conference | 17th International Conference on Information Systems for Crisis Response and Management | |
| Notes | sandrine.bubendorff@telecom-paristech.fr | Approved | no | ||
| Call Number | Serial | 2271 | |||
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| Author | Sara Barozzi; Jose Luis Fernandez Marquez; Amudha Ravi Shankar; Barbara Pernici | ||||
| Title | Filtering images extracted from social media in the response phase of emergency events | Type | Conference Article | ||
| Year | 2019 | Publication | Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2019 |
| Volume | Issue | Pages | |||
| Keywords | rapid mapping, floods, information extraction, filtering, crowdsourcing | ||||
| Abstract | The use of social media to support emergency operators in the first hours of the response phases can improve the quality of the information available and awareness on ongoing emergency events. Social media contain both textual and visual information, in the form of pictures and videos. The problem related to the use of social media posts as a source of information during emergencies lies in the difficulty of selecting the relevant information among a very large amount of irrelevant information. In particular, we focus on the extraction of images relevant to an event for rapid mapping purpose. In this paper, a set of possible filters is proposed and analyzed with the goal of selecting useful images from posts and of evaluating how precision and recall are impacted. Filtering techniques, which include both automated and crowdsourced steps, have the goal of providing better quality posts and easy manageable data volumes both to emergency responders and rapid mapping operators. The impact of the filters on precision and recall in extracting relevant images is discussed in the paper in two different case studies. |
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| Address | Politecnico di Milano;University of Geneva | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Valencia, Spain | Editor | Franco, Z.; González, J.J.; Canós, J.H. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-84-09-10498-7 | Medium | |
| Track | T8- Social Media in Crises and Conflicts | Expedition | Conference | 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) | |
| Notes | Approved | no | |||
| Call Number | Serial | 1881 | |||
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| Author | Sven Schaust; Maximilian Walther; Michael Kaisser | ||||
| Title | Avalanche: Prepare, manage, and understand crisis situations using social media analytics | Type | Conference Article | ||
| Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
| Volume | Issue | Pages | 852-857 | ||
| Keywords | Hardware; Crisis management; Event detection; Natural hazard; Social media analytics; Twitter; Information systems | ||||
| Abstract | The recent rise of Social Media services has created huge streams of information which can be very valuable in a variety of scenarios. One specific scenario that has received interest is how Social Media analytics can be beneficial in crisis situations. In this paper, we describe our vision for a Social Media-ready command and control center. As motivation for our work, we present a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 and we give an overview of the architecture of our event detection subsystem. | ||||
| Address | AGT Group (R and D) GmbH, Germany | ||||
| Corporate Author | Thesis | ||||
| Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
| Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 919 | |||
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| Author | Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer | ||||
| Title | A fine-grained sentiment analysis approach for detecting crisis related microposts | Type | Conference Article | ||
| Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
| Volume | Issue | Pages | 846-851 | ||
| 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 | ||||
| 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. | ||||
| Address | Technische Universität Darmstadt, Germany; Universität Mannheim, Germany | ||||
| Corporate Author | Thesis | ||||
| Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
| Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 927 | |||
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| Author | Seungwon Yang; Haeyong Chung; Xiao Lin; Sunshin Lee; Liangzhe Chen; Andrew Wood; Andrea Kavanaugh; Steven D. Sheetz; Donald J. Shoemaker; Edward A. Fox | ||||
| Title | PhaseVis1: What, when, where, and who in visualizing the four phases of emergency management through the lens of social media | Type | Conference Article | ||
| Year | 2013 | Publication | ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2013 |
| Volume | Issue | Pages | 912-917 | ||
| Keywords | Civil defense; Classification (of information); Data visualization; Information systems; Risk management; 10-fold cross-validation; Classification algorithm; Classification evaluation; Emergency management; Potential utility; ThemeRiver; Through the lens; Twitter; Disasters | ||||
| Abstract | The Four Phase Model of Emergency Management has been widely used in developing emergency/disaster response plans. However, the model has received criticism contrasting the clear phase distinctions in the model with the complex and overlapping nature of phases indicated by empirical evidence. To investigate how phases actually occur, we designed PhaseVis based on visualization principles, and applied it to Hurricane Isaac tweet data. We trained three classification algorithms using the four phases as categories. The 10-fold cross-validation showed that Multi-class SVM performed the best in Precision (0.8) and Naïve Bayes Multinomial performed the best in F-1 score (0.782). The tweet volume in each category was visualized as a ThemeRiver[TM], which shows the 'What' aspect. Other aspects – 'When', 'Where', and 'Who' – Are also integrated. The classification evaluation and a sample use case indicate that PhaseVis has potential utility in disasters, aiding those investigating a large disaster tweet dataset. | ||||
| Address | Department of Computer Science, Virginia Tech, Blacksburg, VA 24061, United States; Department of Accounting and Information Systems, Virginia Tech, Blacksburg, VA 24061, United States; Department of Sociology, Virginia Tech, Blacksburg, VA 24061, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | Karlsruher Institut fur Technologie | Place of Publication | KIT; Baden-Baden | Editor | T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9783923704804 | Medium | |
| Track | Social Media | Expedition | Conference | 10th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1122 | |||
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| Author | Yongzhong Sha; Jinsong Yan; Guoray Cai | ||||
| Title | Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog | Type | Conference Article | ||
| Year | 2014 | Publication | ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2014 |
| Volume | Issue | Pages | 722-726 | ||
| Keywords | Air pollution; Information systems; Time series analysis; Crisis; Pm2.5; Public opinions; Sentiment analysis; Social media analysis; Social aspects | ||||
| Abstract | Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorithm together with a special sentiment words dictionary tailored and refined for Chinese language. The results of time series analysis on detected sentiment strength showed that less than one percent of the posts are strong-positive or strong negative. Weekly sentiment strength measures show symmetric changes in positive and negative strength, but overall trend moved towards more positive opinions. Special attention was given to sharp bursts of sentiment strength that coincide temporally with the occurrence of extreme social events. These findings suggest that sentiment strength analysis may generate useful alert and awareness of pending extreme social events. | ||||
| Address | Lanzhou University, Gansu, China; Penn State University, University Park, PA, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | The Pennsylvania State University | Place of Publication | University Park, PA | Editor | S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9780692211946 | Medium | |
| Track | Social Media in Crisis Response and Management | Expedition | Conference | 11th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 939 | |||
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| Author | Shalini Priya; Manish Bhanu; Sourav Kumar Dandapat; Joydeep Chandra | ||||
| Title | Mirroring Hierarchical Attention in Adversary for Crisis Task Identification: COVID-19, Hurricane Irma | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 609-620 | ||
| Keywords | Covid-19, Hurricane, Adversarial, Hierarchical attention, Support, Infrastructure Damage | ||||
| Abstract | A surge of instant local information on social media serves as the first alarming tone of need, supports, damage information, etc. during crisis. Identifying such signals primarily helps in reducing and suppressing the substantial impacts of the outbreak. Existing approaches rely on pre-trained models with huge historic information as well ason domain correlation. Additionally, existing models are often task specific and need auxiliary feature information.Mitigating these limitations, we introduce Mirrored Hierarchical Contextual Attention in Adversary (MHCoA2) model that is capable to operate under varying tasks of different crisis incidents. MHCoA2 provides attention by capturing contextual correlation among words to enhance task identification without relying on auxiliary information.The use of adversarial components and an additional feature extractor in MHCoA2 enhances its capability to achievehigher performance. MHCoA2 reports an improvement of 5-8% in terms of standard metrics on two real worldcrisis incidents over state-of-the-art. | ||||
| Address | Indian Institute of Technology Patna; Indian Institute of Technology Patna; Indian Institute of Technology Patna; Indian Institute of Technology Patna | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | shalini.pcs16@iitp.ac.in | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2359 | ||
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| Author | Gayane Shalunts; Gerhard Backfried; Prinz Prinz | ||||
| Title | Sentiment analysis of German social media data for natural disasters | Type | Conference Article | ||
| Year | 2014 | Publication | ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2014 |
| Volume | Issue | Pages | 752-756 | ||
| Keywords | Disasters; Information systems; First responders; Integral part; Media analysis; Multiple languages; Natural disasters; Sentiment analysis; Social media; Social media datum; Data mining | ||||
| Abstract | Analysis of social media and traditional media provides significant information to first responders in times of natural disasters. Sentiment analysis, particularly of social media originating from the affected population, forms an integral part of multifaceted media analysis. The current paper extends an existing methodology to the domain of natural disasters, broadens the support of multiple languages and introduces a new manner of classification. The performance of the approach is evaluated on a recently collected dataset manually annotated by three human annotators as a reference. The experiments show a high agreement rate between the approach taken and the annotators. Furthermore, the paper presents the initial application of the resulting technology and models to sentiment analysis of social media data in German, covering data collected during the Central European floods of 2013. | ||||
| Address | SAIL LABS Technology AG, Vienna, Austria | ||||
| Corporate Author | Thesis | ||||
| Publisher | The Pennsylvania State University | Place of Publication | University Park, PA | Editor | S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9780692211946 | Medium | |
| Track | Social Media in Crisis Response and Management | Expedition | Conference | 11th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 940 | |||
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| Author | Shane Errol Halse; Andrea Tapia; Anna Squicciarini; Cornelia Caragea | ||||
| Title | Tweet Factors Influencing Trust and Usefulness During Both Man-Made and Natural Disasters | Type | Conference Article | ||
| Year | 2016 | Publication | ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2016 |
| Volume | Issue | Pages | |||
| Keywords | Twitter; Sandy; Hurricane; Boston Bombing; Trust; Usefulness | ||||
| Abstract | To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the usefulness of these messages in the crisis response domain. Toward detecting useful messages in case of crisis, in this paper, we characterize tweets, which are perceived useful or trustworthy, and determine their main features. Our analysis is carried out on two datasets (one natural and one man made) gathered from Twitter concerning hurricane Sandy in 2012 and the Boston Bombing 2013. The results indicate that there is a high correlation and similar factors (support for the victims, informational data, use of humor and type of emotion used) influencing trustworthiness and usefulness for both disaster types. This could have impacts on how messages from social media data are analyzed for use in crisis response. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| 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 |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 1403 | |||
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| Author | Shane Errol Halse; Aurélie Montarnal; Andrea Tapia; Frederick Benaben | ||||
| Title | Bad Weather Coming: Linking social media and weather sensor data | 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 |
| Volume | Issue | Pages | 507-515 | ||
| Keywords | Twitter; weather; sensor data; social media | ||||
| Abstract | In this paper we leverage the power of citizen supplied data. We examined how both physical weather sensor data (obtained from the weather underground API) and social media data (obtained from Twitter) can serve to improve local community awareness during a severe weather event. A local tornado warning was selected due to its small scale and isolated geographic area, and only Twitter data found from within this geo-locational area was used. Our results indicate that during a severe weather event, an increase in weather activity obtained from the local weather sensors does correlate with an increase in local social media usage. The data found on social media also contains additional information from, and about the community of interest during the event. While this study focuses on a small scale event, it provides the groundwork for use during a much larger weather event. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Rochester Institute of Technology | Place of Publication | Rochester, NY (USA) | Editor | Kees Boersma; Brian Tomaszeski |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-0-692-12760-5 | Medium | |
| 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 | 2127 | |||
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| Author | Shane Errol Halse; Rob Grace; Jess Kropczynski; Andrea Tapia | ||||
| Title | Simulating real-time Twitter data from historical datasets | Type | Conference Article | ||
| Year | 2019 | Publication | Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management | Abbreviated Journal | Iscram 2019 |
| Volume | Issue | Pages | |||
| Keywords | Twitter, Simulation, Crisis Response, Social Media | ||||
| Abstract | In this paper, we will discuss a system design for simulating social media data based on historical datasets. While many datasets containing data collected from social media during crisis have become publicly available, there is a lack of tools or systems can present this data on the same timeline as it was originally posted. Through the design and use of the tool discussed in this paper, we show how historical datasets can be used for algorithm testing, such as those used in machine learning, to improve the quality of the data. In addition, the use of simulated data also has its benefits in training scenarios, which would allow participants to see real, non-fabricated social media messages in the same temporal manner as found on a social media platform. Lastly, we will discuss the positive reception and future improvements suggested by 911 Public Service Answering Point (PSAP) professionals. | ||||
| Address | PSU, United States of America;University of Cincinnati | ||||
| Corporate Author | Thesis | ||||
| Publisher | Iscram | Place of Publication | Valencia, Spain | Editor | Franco, Z.; González, J.J.; Canós, J.H. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-84-09-10498-7 | Medium | |
| Track | T8- Social Media in Crises and Conflicts | Expedition | Conference | 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019) | |
| Notes | Approved | no | |||
| Call Number | Serial | 1898 | |||
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| Author | Shane Halse; Jomara Binda; Samantha Weirman | ||||
| Title | It's what's outside that counts: Finding credibility metrics through non-message related Twitter features | 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 |
| Volume | Issue | Pages | 516-528 | ||
| Keywords | Twitter; social media; trust | ||||
| Abstract | Social media data, such as Twitter, enables crisis response personnel and civilians to share information during a crisis situation. However, a lack of information gatekeeping processes also translates into concerns about both content and source credibility. This research aims to identify Twitter metrics which could assist with the latter. A 2 (average number of hashtags used) x 2 (ratio of tweets/retweets posted) x 2 (ratio of follower/followee) between-subjects experiment was conducted to evaluate the level of influence of Twitter broker metrics on behavioral intention and the perception of source credibility. The findings indicate that follower/followee ratio in conjunction with hashtag usage approached a significant effect on perceived source credibility. In addition, both Twitter awareness metrics and dispositional trust played an important role in determining behavioral intentions and perceived source credibility. Implications and limitations are also discussed. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Rochester Institute of Technology | Place of Publication | Rochester, NY (USA) | Editor | Kees Boersma; Brian Tomaszeski |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-0-692-12760-5 | Medium | |
| 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 | 2128 | |||
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| Author | Shannon Daly; James A. Thom | ||||
| Title | Mining and Classifying Image Posts on Social Media to Analyse Fires | Type | Conference Article | ||
| Year | 2016 | Publication | ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2016 |
| Volume | Issue | Pages | |||
| Keywords | Flickr; Image Analytics; Geotags; Geocoding | ||||
| Abstract | We propose a methodology to study the occurrence of fires through image posts on Flickr; crowd-sourcing information from a noisy social media dataset can estimate the presence of fires. We collect several years worth of photos and associated metadata using fire-related search terms. We use an image classification model to detect geotagged photos that are further analysed to determine if a fire event did occur at a particular time and place. Furthermore, a case study investigates image features and spatio-temporal elements in the metadata, as well as location information contained in camera EXIF data. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| 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 |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3388 | ISBN | 978-84-608-7984-9 | Medium | |
| Track | Social Media Studies | Expedition | Conference | 13th International Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | ISCRAM @ idladmin @ | Serial | 1395 | ||
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| Author | Patrick C. Shih; Kyungsik Han; John M. Carroll | ||||
| Title | Community incident chatter: Informing local incidents by aggregating local news and social media content | Type | Conference Article | ||
| Year | 2014 | Publication | ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | ISCRAM 2014 |
| Volume | Issue | Pages | 772-776 | ||
| Keywords | Hardware; Civic awareness and participation; Large-scale event; Local incidents and emergencies; News agencies; Smart-phone applications; Social media; Two way communications; User study; Information systems | ||||
| Abstract | The emergence of social media provides an additional channel for broadcasting information to the public and support two-way communication between governmental stakeholders and the public during crisis. Research has focused on large-scale events, and few have investigated how social media can contribute to civic awareness and participation of small-scale incidents in a community-oriented context. Moreover, social media have been criticized because it is overabundant with noisy, inaccurate, and unprofessional information that are often misleading. This presents a serious challenge for community members to identify information that are relevant to a local incident. We introduce Community Incident Chatter (CIC), a smartphone application that is designed to aggregate information reported by formal news agencies and social media surrounding local incidents. Participants in a preliminary user study indicate that the community-oriented information presented in CIC is informative, relevant to the community, and has the potential of empowering community residents for responding to and managing local incidents. | ||||
| Address | Pennsylvania State University, United States | ||||
| Corporate Author | Thesis | ||||
| Publisher | The Pennsylvania State University | Place of Publication | University Park, PA | Editor | S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih. |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 9780692211946 | Medium | |
| Track | Social Media in Crisis Response and Management | Expedition | Conference | 11th International ISCRAM Conference on Information Systems for Crisis Response and Management | |
| Notes | Approved | no | |||
| Call Number | Serial | 949 | |||
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| Author | Shivam Sharma; Cody Buntain | ||||
| Title | An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts | Type | Conference Article | ||
| Year | 2021 | Publication | ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2021 |
| Volume | Issue | Pages | 808-815 | ||
| Keywords | covid19, twitter, trecis, cross-validation, machine learning, transfer learning | ||||
| Abstract | In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. | ||||
| Address | New Jersey Institute of Technology; New Jersey Institute of Technology | ||||
| Corporate Author | Thesis | ||||
| Publisher | Virginia Tech | Place of Publication | Blacksburg, VA (USA) | Editor | Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language | English | Summary Language | English | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 978-1-949373-61-5 | ISBN | Medium | ||
| Track | Social Media for Disaster Response and Resilience | Expedition | Conference | 18th International Conference on Information Systems for Crisis Response and Management | |
| Notes | cbuntain@njit.edu | Approved | no | ||
| Call Number | ISCRAM @ idladmin @ | Serial | 2375 | ||
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| Author | Shivam Sharma; Cody Buntain | ||||
| Title | Bang for your Buck: Performance Impact Across Choices in Learning Architectures for Crisis Informatics | Type | Conference Article | ||
| Year | 2022 | Publication | ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management | Abbreviated Journal | Iscram 2022 |
| Volume | Issue | Pages | 719-736 | ||
| Keywords | Incident Streams; TREC; TRECIS; crisis informatics | ||||
| Abstract | Over the years, with the increase in social media engagement, there has been an in increase in various pipelines to analyze, classify and prioritize crisis-related data on various social media platforms. These pipelines utilize various data augmentation methods to counter imbalanced crisis data, sophisticated and off-the-shelf models for training. However, there is a lack of comprehensive study which compares these methods for the various sections of a pipeline. In this study, we split a general crisis-related pipeline into 3 major sections, namely, data augmentation, model selection, and training methodology. We compare various methods for each of these sections and then present a comprehensive evaluation of which section to prioritize based on the results from various pipelines. We compare our results against two separate tasks, information classification and priority scoring for crisis-related tweets. Our results suggest that data augmentation, in general,improves the performance. However, sophisticated, state-of-the-art language models like DeBERTa only show performance gain in information classification tasks, and models like RoBERTa tend to show a consistent performance increase over our presented baseline consisting of BERT. We also show that, though training two separate task-specific BERT models does show better performance than one BERT model with multi-task learning methodology over an imbalanced dataset, multi-task learning does improve performance for more sophisticated model like DeBERTa with a much more balanced dataset after augmentation. | ||||
| Address | New Jersey Institute of Technology; New Jersey Institute of Technology | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Tarbes, France | Editor | Rob Grace; Hossein Baharmand | |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 2411-3387 | ISBN | 978-82-8427-099-9 | Medium | |
| Track | Social Media for Crisis Management | Expedition | Conference | ||
| Notes | Approved | no | |||
| Call Number | ISCRAM @ idladmin @ | Serial | 2451 | ||
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| Author | Shuji Nishikawa; Osamu Uchida; Keisuke Utsu | ||||
| Title | Introduction of a Tracking Map to a Web Application for Location Recording and Rescue Request | 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 |
| Volume | Issue | Pages | 459-468 | ||
| Keywords | Location information, Rescue request, Disaster | ||||
| Abstract | We developed a web application for location recording and rescue request using Twitter (T-Pl@ce). This application helps supported users (e.g., older adults, persons with disabilities, and children) who require support to share their location coordinates via Twitter. Supporting users (e.g., families, relatives, or neighbors) of the supported user can then check the location coordinates of the supported user when required. When the supported user needs to be rescued, he/she can post a rescue request on Twitter by pressing the “Rescue request” button on the application. In this study, we introduce the e-mail notification function to reliably notify a rescue request to the system administrator. In addition, to track the location of the supported user, we introduce a location tracking function. Then, the administrator, the emergency assistance employees (e.g., rescue experts or social workers), or the supporting user can refer to the request and the location tracking page and execute the support and rescue activities. | ||||
| Address | Tokai University; Tokai University; Tokai University | ||||
| Corporate Author | Thesis | ||||
| Publisher | Massey Univeristy | Place of Publication | Albany, Auckland, New Zealand | Editor | Kristin Stock; Deborah Bunker |
| Language | English | Summary Language | Original Title | ||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Track | Social Media and Community Engagement Supporting Resilience Building | Expedition | Conference | ||
| Notes | Approved | no | |||
| Call Number | Serial | 1660 | |||
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