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Author |
Zijun Long; Richard McCreadie |
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Title |
Is Multi-Modal Data Key for Crisis Content Categorization on Social Media? |
Type |
Conference Article |
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Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
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Pages |
1068-1080 |
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Keywords |
Social Media Classification; Multi-modal Learning; Crisis Management; Deep Learning, BERT; Supervised Learning |
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Abstract |
The user-base of social media platforms, like Twitter, has grown dramatically around the world over the last decade. As people post everything they experience on social media, large volumes of valuable multimedia content are being recorded online, which can be analysed to help for a range of tasks. Here we specifically focus on crisis response. The majority of prior works in this space focus on using machine learning to categorize single-modality content (e.g. text of the posts, or images shared), with few works jointly utilizing multiple modalities. Hence, in this paper, we examine to what extent integrating multiple modalities is important for crisis content categorization. In particular, we design a pipeline for multi-modal learning that fuses textual and visual inputs, leverages both, and then classifies that content based on the specified task. Through evaluation using the CrisisMMD dataset, we demonstrate that effective automatic labelling for this task is possible, with an average of 88.31% F1 performance across two significant tasks (relevance and humanitarian category classification). while also analysing cases that unimodal models and multi-modal models success and fail. |
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University of Glasgow; University of Glasgow |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
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Language |
English |
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Edition |
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ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
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Track |
Social Media for Crisis Management |
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Conference |
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no |
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Call Number |
ISCRAM @ idladmin @ |
Serial |
2472 |
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Author |
Anthony C. Robinson; Alexander Savelyev; Scott Pezanowski; Alan M. MacEachren |
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Title |
Understanding the utility of geospatial information in social media |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
918-922 |
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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 |
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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. |
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Address |
Department of Geography, GeoVISTA Center, Penn State University, United States |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
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Track |
Social Media |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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no |
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Call Number |
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883 |
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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 |
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Title |
PhaseVis1: What, when, where, and who in visualizing the four phases of emergency management through the lens of social media |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
912-917 |
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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 |
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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. |
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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 |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Abbreviated Series Title |
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Series Volume |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
1122 |
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Author |
Daniel E. Lane; Tracey L. O'Sullivan; Craig E. Kuziemsky; Fikret Berkes; Anthony Charles |
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Title |
A structured equation model of collaborative community response |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
906-911 |
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Keywords |
Computer simulation; Decision theory; Information systems; Mathematical models; Risk analysis; Adaptation; C-change; Community collaboration; Community engagement; Emergency response; EnRiCH; Preparedness; Simulation; Structured equation modeling; Emergency services |
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Abstract |
This paper analyses the collaborative dynamic of community in response to urgent situations. Community emergencies arising from natural or man-induced threats are considered as exogenous events that stimulate community resources to be unified around the response, action, and recovery activities related to the emergency. A structured equation model is derived to depict the actions of the community system. The system is described in terms of its resources including the propensity to trigger community action and collaboration among diverse groups. The community is profiled with respect to its ability to respond. The system defines the trigger mechanisms that are considered to be the drivers of collaborative action. A simulation model is presented to enact the system emergencies, community profiles, and collaborative response. The results develop an improved understanding of conditions that engage community collaborative actions as illustrated by examples from community research in the EnRiCH and the C-Change community research projects. |
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Address |
Telfer School of Management, University of Ottawa, Canada; Interdisciplinary Faculty of Health Sciences, University of Ottawa, Canada; Natural Resources Institute, University of Manitoba, Canada; Department of Finance and Management Science, Saint Mary's University, Canada |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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Abbreviated Series Title |
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Series Volume |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Approved |
no |
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Call Number |
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Serial |
677 |
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Author |
Manne Messemaker; Jeroen Wolbers; Willem Treurniet; Kees Boersma |
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Title |
Shaping societal impact: Between control and cooperation |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
901-905 |
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Keywords |
Information systems; Command and control; Crisis communications; Crisis management; Incident response; Mutual shaping; Security incident; Societal impacts; Concretes |
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Abstract |
In our modem society, the impact of large-scale spfety and security incidents can be large and diverse. Yet. this societal impact is makeable and controllable to a limited extent. At best, the effect of concrete response actions is that the direct damage is somewhat reduced and that the recovery is accelerated. Proper crisis communication can make the biggest difference with respect to overall societal impact. We argue that crisis communication must strike a balance between a directive approach of chaos, command and control and a more empathic approach of continuity, coordination and cooperation. On the basis of a concrete case we analyze how crisis communication reflects the incident response approach and how societal impact is affected. |
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Address |
VU University Amsterdam, Netherlands; TNO, Netherlands |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
770 |
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Author |
Zeno Franco; Syed Ahmed; Craig E. Kuziemsky; Paul A. Biedrzycki; Anne Kissack |
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Title |
Using social network analysis to explore issues of latency, connectivity, interoperability & sustainability in community disaster response |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
896-900 |
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Keywords |
Data fusion; Disasters; Information systems; Mergers and acquisitions; Social networking (online); Boundary spanning; Community engagement; Community resources; Community vulnerability; Crisis response; Disaster recovery; Disaster response; Social network analysis approaches; Emergency services |
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Abstract |
Community-based disaster response is gaining attention in the United States because of major problems with domestic disaster recovery over the last decade. A social network analysis approach is used to illustrate how community-academic partnerships offer one way to leverage information about existing, mediated relationships with the community through trusted actors. These partnerships offer a platform that can be used to provide entré into communities that are often closed to outsiders, while also allowing greater access to community embedded physical assets and human resources, thus facilitated more culturally appropriate crisis response. Using existing, publically available information about funded community-academic partnerships in Wisconsin, USA, we show how social network analysis of these meta-organizations may provide critical information about both community vulnerabilities in disaster and assist in rapidly identifying these community resources in the aftermath of a crisis event that may provide utility for boundary spanning crisis information systems. |
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Address |
Medical College of Wisconsin, United States; U. Ottawa, Canada; City of Milwaukee Public Health Department, United Kingdom |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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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 |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
504 |
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Author |
Xukun Li; Doina Caragea |
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Title |
Improving Disaster-related Tweet Classification with a Multimodal Approach |
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 |
893-902 |
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Keywords |
Multimodal Model; Tweet Classification; Deep Learning |
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Abstract |
Social media data analysis is important for disaster management. Lots of prior studies have focused on classifying a tweet based on its text or based on its images, independently, even if the tweet contains both text and images. Under the assumptions that text and images may contain complementary information, it is of interest to construct classifiers that make use of both modalities of the tweet. Towards this goal, we propose a multimodal classification model which aggregates text and image information. Our study aims to provide insights into the benefits obtained by combining text and images, and to understand what type of modality is more informative with respect to disaster tweet classification. Experimental results show that both text and image classification can be improved by the multimodal approach. |
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Address |
Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University |
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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 |
978-1-949373-27-79 |
ISBN |
2411-3465 |
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 |
xukun@ksu.edu |
Approved |
no |
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Call Number |
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Serial |
2280 |
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Author |
Tracey L. O'Sullivan; Wayne Corneil; Craig E. Kuziemsky; Daniel E. Lane |
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Title |
Citizen participation in the specification and mapping of potential disaster assets |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
890-895 |
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Keywords |
Disaster prevention; Disasters; Information systems; Innovation; Asset-mapping; Collaboration; Empowerment; Engagement; Resilience; Mapping |
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Abstract |
Asset-mapping is a strategy used in disaster preparedness planning, however participation is typically limited to a small number of organizations with specific expertise related to disaster response. Broader strategies are needed to ensure identification of assets is comprehensive and to stimulate innovative thinking about which attributes of a community are potential assets for response and recovery. As part of The EnRiCH Project intervention, asset-mapping was used as a collaborative activity to promote identification of a broad range of assets which could be used to enhance resilience and promote preparedness among high risk populations. In this paper we present a study (in progress) which explores innovation and empowerment among a collaborative community group in Canada. Qualitative content analysis was used to analyze focus group transcripts from 2 sessions where the participants (n=18) learned how to use google docs and create a database of community assets, while developing collaborative relationships. |
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Address |
Interdisciplinary School of Health Sciences, University of Ottawa, Canada; Institute of Population Health, University of Ottawa, Canada; Telfer School of Management, University of Ottawa, Canada |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
819 |
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Author |
Christian Reuter |
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Title |
Power outage communications: Survey of needs, infrastructures and concepts |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
884-889 |
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Keywords |
Information systems; Mobile computing; Citizen; Communication infrastructure; Crisis communications; General information; Information and Communication Technologies; Information demand; Power outage; Smart-phone applications; Outages |
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Abstract |
Crisis communication during power outages poses several challenges. Frist, the causes of power outages are often events such as severe weather, which also lead to complications. Second, power outages themselves lead to limitations in everyday life. Third, communication infrastructures, that are necessary for crisis communication, are often affected. This work focuses on the communication of the organizations responsible for recovery work (emergency services, public administration, energy network operators) to the public affected by the power outage. Therefore this paper investigates the perception and the information demands of citizens and communication infrastructures in different scenarios. Taking the users' needs into consideration, an Information and Communication Technology (ICT) based concept for crisis communication, which combines general information with location-specific and setting-specific information was implemented as a prototype smartphone application and evaluated with 12 potential end users. ICT-based concepts can gain acceptance, however they should be understood as supplemental for some target groups and in some scenarios. |
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Address |
Institute for Information Systems, University of Siegen, Germany |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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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 |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
873 |
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Author |
Nasik Muhammad Nafi; Avishek Bose; Sarthak Khanal; Doina Caragea; William H. Hsu |
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Title |
Abstractive Text Summarization of Disaster-Related Documents |
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 |
881-892 |
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Keywords |
Disaster Reporting; Text Summarization; Information Extraction; Reinforcement Learning; Evaluation Metrics |
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Abstract |
Abstractive summarization is intended to capture key information from the full text of documents. In the application domain of disaster and crisis event reporting, key information includes disaster effects, cause, and severity. While some researches regarding information extraction in the disaster domain have focused on keyphrase extraction from short disaster-related texts like tweets, there is hardly any work that attempts abstractive summarization of long disaster-related documents. Following the recent success of Reinforcement Learning (RL) in other domains, we leverage an RL-based state-of-the-art approach in abstractive summarization to summarize disaster-related documents. RL enables an agent to find an optimal policy by maximizing some reward. We design a novel hybrid reward metric for the disaster domain by combining \underline{Vec}tor Similarity and \underline{Lex}icon Matching (\textit{VecLex}) to maximize the relevance of the abstract to the source document while focusing on disaster-related keywords. We evaluate the model on a disaster-related subset of a CNN/Daily Mail dataset consisting of 104,913 documents. The results show that our approach produces more informative summaries and achieves higher \textit{VecLex} scores compared to the baseline. |
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Address |
Kansas State University; Kansas State University; Kansas State University; Kansas State University; Kansas State University |
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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 |
978-1-949373-27-78 |
ISBN |
2411-3464 |
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 |
nnafi@ksu.edu |
Approved |
no |
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Call Number |
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Serial |
2279 |
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Author |
Jakob Rogstadius; Claudio Teixeira; Evangelos Karapanos; Vassilis Kostakos |
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Title |
An introduction for system developers to volunteer roles in crisis response and recovery |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
874-883 |
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Keywords |
Computer software; Information management; Collaboration; Crisis management; Developer guidelines; Disaster response; System development; Volunteering; Information systems |
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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. |
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Address |
Madeira Interactive Technologies Institute, Portugal; University of Oulu, Finland |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
885 |
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Author |
Matti Wiegmann; Jens Kersten; Friederike Klan; Martin Potthast; Benno Stein |
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Title |
Analysis of Detection Models for Disaster-Related Tweets |
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 |
872-880 |
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Keywords |
Tweet Filtering; Crisis Management; Evaluation Framework |
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Abstract |
Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus~2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46~disasters covering 9~disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of~3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability. |
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Address |
Bauhaus-Universit\“at Weimar German Aerospace Center (DLR); German Aerospace Center (DLR); German Aerospace Center (DLR); Leipzig University; Bauhaus-Universit\”at Weimar |
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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 |
978-1-949373-27-77 |
ISBN |
2411-3463 |
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 |
matti.wiegmann@uni-weimar.de |
Approved |
no |
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Call Number |
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Serial |
2278 |
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Author |
André Dittrich; Christian Lucas |
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Title |
A step towards real-time analysis of major disaster events based on tweets |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
868-874 |
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Keywords |
Information systems; Semantics; Social networking (online); Crisis management; Event detection; Functional model; Micro-blogging platforms; Real time analysis; Semantic content analysis; Social sensors; Twitter; Disasters |
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Abstract |
The most popular micro blogging platform Twitter has been the topic of a variety of research papers related to disaster and crisis management. As an essential first step and basis for a real-time methodology to exploit Twitter for event detection, localization and ultimately semantic content analysis, a functional model to describe the amount of tweets during a day has been developed. It was derived from a corpus of messages in an exemplary area of investigation. To satisfy the different daily behavior on particular days, two types of days are distinguished in this paper. Moreover, keyword-adjusted data is used to point out the potential of semantic tweet analysis in following steps. The consideration of spatial event descriptions in relevant tweets could significantly improve and accelerate the perception of a disaster. The results from the conducted tests demonstrate the capability of the functional model to detect events with significant social impact in Twitter data. |
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Address |
Karlsruhe Institute of Technology (KIT), Germany |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
452 |
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Author |
Daniel Iland; Don Voita; Elizabeth Belding |
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Title |
Delay tolerant disaster communication with the One Laptop per Child XO laptop |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
863-867 |
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Keywords |
Disasters; Information systems; Internet; MESH networking; Delay Tolerant Networking; Disaster communications; Epidemic routing; Information sharing; Olpc; Peer to peer; Situational awareness; Telepathy salut; Ushahidi; Laptop computers |
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Abstract |
In this paper, we describe the design, implementation, and evaluation of a mesh network based messaging application for the One Laptop Per Child XO laptop. We outline the creation of an easy-to-use OLPC Activity that exchanges Ushahidi-style messages with nearby OLPC users through the Internet or a mesh network. Our contributions are to implement an epidemic messaging scheme on mesh networks of OLPC XO laptops, to extend the Ushahidi web application to efficiently exchange messages with nodes in mesh networks, and to allow the Ushahidi server to distribute cures, notifications of message delivery, for each received message. Testing and analysis revealed substantial overhead is introduced by the OLPC's use of Telepathy Salut for activity sharing. |
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Address |
University of California, Santa Barbara, United States |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
611 |
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Author |
Anna Kruspe |
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Title |
Detecting Novelty in Social Media Messages During Emerging Crisis Events |
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 |
860-871 |
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Keywords |
Social media; Clustering; Novelty; Embeddings |
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Abstract |
Social media can be a highly valuable source of information during disasters. A crisis' development over time is of particular interest here, as social media messages can convey unfolding events in near-real time. Previous approaches for the automatic detection of information in such messages have focused on a static analysis, not taking temporal changes and already-known information into account. In this paper, we present a novel method for detecting new topics in incoming Twitter messages (tweets) conditional upon previously found related tweets. We do this by first extracting latent representations of each tweet using pre-trained sentence embedding models. Then, Infinite Mixture modeling is used to dynamically cluster these embeddings anew with each incoming tweet. Once a cluster reaches a minimum number of members, it is considered to be a new topic. We validate our approach on the TREC Incident Streams 2019A data set. |
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Address |
German Aerospace Center (DLR), Jena, Germany |
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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 |
978-1-949373-27-76 |
ISBN |
2411-3462 |
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 |
anna.kruspe@dlr.de |
Approved |
no |
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Call Number |
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Serial |
2277 |
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Author |
David F. Merrick; Tom Duffy |
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Title |
Utilizing community volunteered information to enhance disaster situational awareness |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
858-862 |
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Keywords |
Civil defense; Disasters; Information systems; Risk management; Social networking (online); Community volunteered information; Crowd sourcing; Facebook; Situational awareness; Social media; Twitter; Emergency services |
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Abstract |
Social media allows the public to engage in the disaster response and recovery process in new and exciting ways. Many emergency management agencies in the United States are embracing social media as a new channel for alerts, warnings, and public outreach, but very few are mining the massive amounts of data available for use in disaster response. The research reflected in this paper strives to help emergency management practitioners harness the power of community volunteered information in a way that is still novel in most parts of the country. Field verification and research combined with survey results attempts to identify and solve many of the barriers to adoption that currently exist. By helping practitioners understand the virtues and limitations of this type of data and information, this research will encourage the use of community volunteered information in the emergency operations center. |
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Address |
Florida State University, United States |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
767 |
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Author |
Sven Schaust; Maximilian Walther; Michael Kaisser |
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Title |
Avalanche: Prepare, manage, and understand crisis situations using social media analytics |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
852-857 |
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Keywords |
Hardware; Crisis management; Event detection; Natural hazard; Social media analytics; Twitter; Information systems |
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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. |
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Address |
AGT Group (R and D) GmbH, Germany |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
919 |
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Author |
Rob Grace |
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Title |
Hyperlocal Toponym Usage in Storm-Related Social Media |
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 |
849-859 |
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Keywords |
Volunteered Geographic Information, Twitter, Information Behavior, Crisis Informatics, Emergency Management. |
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Abstract |
Crisis responders need to locate events reported in social media messages that typically lack geographic metadata such as geotags. Toponyms, places names referenced in messages, provide another source of geographic information, however, the availability and granularity of toponyms in crisis social media remain poorly understood. This study examines toponym usage and granularity across six categories of crisis-related information posted on Twitter during a severe storm. Findings show users often include geographic information in messages describing local and remote storm events but do so rarely when discussing other topics, more often use toponyms than geotags when describing local events, and tend to include fine-grained toponyms in reports of infrastructure damage and service disruption and course-grained toponyms in other kinds of storm-related messages. These findings present requirements for hyperlocal geoparsing techniques and suggest that social media monitoring presents more immediate affordances for course-grained damage assessment than fine-grained situational awareness during a crisis. |
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Address |
Texas Tech University |
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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 |
978-1-949373-27-75 |
ISBN |
2411-3461 |
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 |
rob.grace@ttu.edu |
Approved |
no |
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Call Number |
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Serial |
2276 |
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Author |
Axel Schulz; Tung Dang Thanh; Heiko Paulheim; Immanuel Schweizer |
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Title |
A fine-grained sentiment analysis approach for detecting crisis related microposts |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
846-851 |
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Keywords |
Artificial intelligence; Information systems; Learning systems; Risk management; Social networking (online); Amount of information; Emergency management; Microposts; Real-time information; Sentiment analysis; Situational awareness; Systematic evaluation; Twitter; Data mining |
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Abstract |
Real-time information from microposts like Twitter is useful for applications in the crisis management domain. Currently, that potentially valuable information remains mostly unused by the command staff, mainly because the sheer amount of information cannot be handled efficiently. Sentiment analysis has been shown as an effective tool to detect microposts (such as tweets) that contribute to situational awareness. However, current approaches only focus on two or three emotion classes. But using only tweets with negative emotions for crisis management is not always sufficient. The amount of remaining information is still not manageable or most of the tweets are irrelevant. Thus, a more fine-grained differentiation is needed to identify relevant microposts. In this paper, we show the systematic evaluation of an approach for sentiment analysis on microposts that allows detecting seven emotion classes. A preliminary evaluation of our approach in a crisis related scenario demonstrates the applicability and usefulness. |
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Address |
Technische Universität Darmstadt, Germany; Universität Mannheim, Germany |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
927 |
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Author |
Andrea Zielinski; Stuart E. Middleton; Laurissa N. Tokarchuk; Xinyue Wang |
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Title |
Social media text mining and network analysis for decision support in natural crisis management |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
840-845 |
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Keywords |
Arts computing; Decision support systems; Information systems; Software prototyping; Decision supports; Link analysis; Social media; Text mining; Vgi; Web Mining; Data mining |
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Abstract |
A core issue in crisis management is to extract from the mass of incoming information what is important for situational awareness during mass emergencies. Based on a case study we develop a prototypical application, TweetComp1, which is integrated into the decision-support component of a Tsunami early warning system and demonstrates the applicability of our approach. This paper describes four novel approaches using focused twitter crawling, trustworthiness analysis, geo-parsing, and multilingual tweet classification in the context of how they could be used for monitoring crises. The validity of our state-of-the art text mining and network analysis technologies will be verified in different experiments based on a human annotated gold standard corpus. |
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Address |
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation, Karlsruhe, Germany; IT Innovation Centre, University of Southampton, Southampton, United Kingdom; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, United Kingdom |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
1160 |
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Author |
Liuqing Li; Edward A. Fox |
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Title |
Disaster Response Patterns across Different User Groups on Twitter: A Case Study during Hurricane Dorian |
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 |
838-848 |
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Keywords |
Hurricane, Response, Pattern, User Classification, Twitter |
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Abstract |
We conducted a case study analysis of disaster response patterns across different user groups during Hurricane Dorian in 2019. We built a tweet collection about the hurricane, covering a two week period. We divided Twitter users into two groups: brand/organization or individual. We found a significant difference in response patterns between the groups. Brand users increasingly participated as the disaster unfolded, and they posted more tweets than individual users on average. Regarding emotions, brand users posted more tweets with joy and surprise, while individual users posted more tweets with sadness. Fear was a common emotion between the two groups. Further, both groups used different types of hashtags and words in their tweets. Some distinct patterns were also discovered in their concerns on specific topics. These results suggest the value of further exploration with more tweet collections, considering the behavior of different user groups during disasters. |
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Address |
Department of Computer Science, Virginia Tech; Department of Computer Science, Virginia Tech; |
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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 |
978-1-949373-27-74 |
ISBN |
2411-3460 |
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 |
liuqing@vt.edu |
Approved |
no |
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Call Number |
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Serial |
2275 |
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Author |
Soudip Roy Chowdhury; Muhammad Imran; Muhammad Rizwan Asghar; Amer-Yahia, S.; Carlos Castillo |
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Title |
Tweet4act: Using incident-specific profiles for classifying crisis-related messages |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
834-839 |
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Keywords |
Artificial intelligence; Disaster prevention; Classification methods; Crisis informatics; Disaster management; Micro-blogging platforms; Microblogging; Precision and recall; Standard machines; Twitter data-analytics; Information systems |
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Abstract |
We present Tweet4act, a system to detect and classify crisis-related messages communicated over a microblogging platform. Our system relies on extracting content features from each message. These features and the use of an incident-specific dictionary allow us to determine the period type of an incident that each message belongs to. The period types are: Pre-incident (messages talking about prevention, mitigation, and preparedness), during-incident (messages sent while the incident is taking place), and post-incident (messages related to the response, recovery, and reconstruction). We show that our detection method can effectively identify incident-related messages with high precision and recall, and that our incident-period classification method outperforms standard machine learning classification methods. |
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Address |
University of Trento, Italy; Fehler Textmarke Nicht Definiert, University of Trento, Italy; CNRS, LIG, France; QCRI, Doha, Qatar |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
2411-3387 |
ISBN |
9783923704804 |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
396 |
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Author |
Roser Beneito-Montagut; Susan Anson; Duncan Shaw; Christopher Brewster |
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Title |
Governmental social media use for emergency communication |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
828-833 |
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Keywords |
Civil defense; Disasters; Information systems; Risk management; Emergency communication; Emergency management; Governmental agency; Information flows; Institutional resilience; Social media; Web 2.0 tools; Societies and institutions |
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Abstract |
The possibility of crowdsourced information, multi-geographical and multi-organisational information flows during emergencies and crises provided by web 2.0 tools are providing emergency management centres with new communication challenges and opportunities. Building on the existing emergency management and social media literature, this article explores how institutions are using and adopting social media for emergency communication. By examining the drivers and barriers of social media adoption in two European governmental agencies dealing with emergencies, the paper aims to establish a framework to examine whether and how institutional resilience could be improved. |
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Address |
Aston Business School, United Kingdom; Warwick Business School, United Kingdom |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Editor |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
302 |
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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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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 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 |
Starr Roxanne Hiltz; Linda Plotnick |
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Title |
Dealing with information overload when using social media for emergency management: Emerging solutions |
Type |
Conference Article |
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Year |
2013 |
Publication |
ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2013 |
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Volume |
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Issue |
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Pages |
823-827 |
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Keywords |
Civil defense; Information systems; Natural language processing systems; Risk management; Decision making process; Emergency management; Emergency response; Information overloads; NAtural language processing; Social convention; Social media; Trending topics; Disasters |
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Abstract |
Several recent studies point the way to enabling emergency response managers to be able to find relevant posts and incorporate them into their sensemaking and decision making processes. Among the approaches that have improved the ability to find the most relevant information are the social conventions of creating topic groups and tags and of “retweeting;” the use of trained volunteers to filter and summarize posts for responders; automated notifications of trending topics; natural language processing of posts; techniques for identifying posts from the disaster site; and the use of GIS and crisis maps to visually represent the distribution of incidents. |
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Address |
NJIT, Newark NJ, United States; Jacksonville State U., AL, United States |
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Corporate Author |
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Thesis |
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Publisher |
Karlsruher Institut fur Technologie |
Place of Publication |
KIT; Baden-Baden |
Editor |
T. Comes, F. Fiedrich, S. Fortier, J. Geldermann and T. Müller |
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Language |
English |
Summary Language |
English |
Original Title |
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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 |
9783923704804 |
Medium |
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Track |
Social Media |
Expedition |
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Conference |
10th International ISCRAM Conference on Information Systems for Crisis Response and Management |
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Notes |
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Approved |
no |
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Call Number |
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Serial |
583 |
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