ISCRAM Digital Library -- Query Results

	ISCRAM Digital Library Home \| Show All \| Simple Search \| Advanced Search	Login Quick Search: Field: contains: ...
	1–11 of 11 records found matching your query (RSS):

Search & Display Options

Select All Deselect All

<< 1 >>

List View

Citations

Details

	Records
	Author	Congcong Wang; Paul Nulty; David Lillis
	Title	Transformer-based Multi-task Learning for Disaster Tweet Categorisation			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	705-718
	Keywords	Disaster Response, Tweet Analysis, Transformers, Natural Language Processing
	Abstract	Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can contribute to the situational awareness of emergency responders, who have a need for them to be categorised according to information types (i.e. the type of aid services the messages are requesting). We introduce a transformer-based multi-task learning (MTL) technique for classifying information types and estimating the priority of these messages. We evaluate the effectiveness of our approach with a variety of metrics by submitting runs to the TREC Incident Streams (IS) track: a research initiative specifically designed for disaster tweet classification and prioritisation. The results demonstrate that our approach achieves competitive performance in most metrics as compared to other participating runs. Subsequently, we find that an ensemble approach combining disparate transformer encoders within our approach helps to improve the overall effectiveness to a significant extent, achieving state-of-the-art performance in almost every metric. We make the code publicly available so that our work can be reproduced and used as a baseline for the community for future work in this domain.
	Address	University College Dublin; University College Dublin; University College Dublin
	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	wangcongcongcc@gmail.com			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2366
Share this record to Facebook



	Author	Hristo Tanev; Vanni Zavarella; Josef Steinberger
	Title	Monitoring disaster impact: detecting micro-events and eyewitness reports in mainstream and 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	592-602
	Keywords	Natural language processing; machine learning; crisis computing; disaster effects; social media
	Abstract	This paper approaches the problem of monitoring the impact of the disasters by mining web sources for the events, caused by these disasters. We refer to these disaster effects as “micro-events”. Micro-events typically following a large disaster include casualties, damage on infrastructures, vehicles, services and resource supply, as well as relief operations. We present natural language grammar learning algorithms which form the basis for building micro-event detection systems from data, with no or minor human intervention, and we show how they can be applied to mainstream news and social media for monitoring disaster impact. We also experimented with applying statistical classifiers to distill, from social media situational updates on disasters, eyewitness reports from directly affected people. Finally, we describe a Twitter mining robot, which integrates some of these monitoring techniques and is intended to serve as a multilingual content hub for enhancing situational awareness.
	Address	European Commission Joint Research Centre; University of West Bohemia
	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	2047
Share this record to Facebook



	Author	Hussein Mouzannar; Yara Rizk; Mariette Awad
	Title	Damage Identification in Social Media Posts using Multimodal Deep Learning			Type	Conference Article
	Year	2018	Publication	ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2018
	Volume		Issue		Pages	529-543
	Keywords	Humanitarian computing, deep neural networks, multimodal learning, natural language processing, visual object recognition.
	Abstract	Social media has recently become a digital lifeline used to relay information and locate survivors in disaster situations. Currently, officials and volunteers scour social media for any valuable information; however, this approach is implausible as millions of posts are shared by the minute. Our goal is to automate actionable information extraction from social media posts to efficiently direct relief resources. Identifying damage and human casualties allows first responders to efficiently allocate resources and save as many lives as possible. Since social media posts contain text, images and videos, we propose a multimodal deep learning framework to identify damage related information. This framework combines multiple pretrained unimodal convolutional neural networks that extract features from raw text and images independently, before a final classifier labels the posts based on both modalities. Experiments on a home-grown database of labeled social media posts showed promising results and validated the merits of the proposed approach.
	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	2129
Share this record to Facebook



	Author	Kelli de Faria Cordeiro; Maria Luiza M Campos; Marcos R. S. Borges
	Title	Adaptive integration of information supporting decision making: A case on humanitarian logistic			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	225-229
	Keywords	Artificial intelligence; Decision support systems; Information systems; Natural language processing systems; Graph database; Heterogeneous information; Humanitarian logistics; Knowledge integration; Linked open datum; Multi-perspective views; Relational data models; Semantic representation; Decision making
	Abstract	There is an urgent demand for information systems to gather heterogeneous information about needs, donations and warehouse stocks to provide an integrated view for decision making in humanitarian logistics. The dynamic flow of information, due to the unpredicted events, requires adaptive features. The traditional relational data model is not suitable due to its schema rigidity. As an alternative, Graph Data models complemented by semantic representations, like Linked Open Data on the Web, can be used. Based on both, this research proposes an approach for the adaptive integration of information and an associated architecture. An application example is discussed in a real scenario where relief goods are managed through a dynamic and multi-perspective view.
	Address	Center for Naval Systems Analysis of Brazilian Navy, Federal University of Rio de Janeiro, Brazil; Federal University of Rio de Janeiro, Brazil
	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	Decision Support Systems	Expedition		Conference	11th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	414
Share this record to Facebook



	Author	Ly Dinh; Sumeet Kulkarni; Pingjing Yang; Jana Diesner
	Title	Reliability of Methods for Extracting Collaboration Networks from Crisis-related Situational Reports and Tweets			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	181-195
	Keywords	Collaboration Networks; Natural Language Processing; Interorganizational Collaboration; Situational Awareness
	Abstract	Assessing the effectiveness of crisis response is key to improving preparedness and adapting policies. One method for response evaluation is reviewing actual response activities and interactions. Response reports are often available in the form of natural language text data. Analyzing a large number of such reports requires automated or semi automated solutions. To improve the trustworthiness of methods for this purpose, we empirically validate the reliability of three relation extraction methods that we used to construct interorganizational collaboration networks by comparing them against human-annotated ground truth (crisis-specific situational reports and tweets). For entity extraction, we find that using a combination of two off-the-shelf methods (FlairNLP and SpaCy) is optimal for situational reports data and one method (SpaCy) for tweets data. For relation extraction, we find that a heuristics-based model that we built by leveraging word co-occurrence and deep and shallow syntax as features and training it on domain-specific text data outperforms two state-of-the-art relation extraction models (Stanford OpenIE and OneIE) that were pre-trained on general domain data. We also find that situational reports, on average, contain less entities and relations than tweets, but the extracted networks are more closely related to collaboration activities mentioned in the ground truth. As it is widely known that general domain tools might need adjustment to perform accurately in specific domains, we did not expect the tested off-the-shelf tools to perform highly accurately. Our point is to rather identify what accuracy one could reasonably expect when leveraging available resources as-is for domain specific work (in this case, crisis informatics), what errors (in terms of false positives and false negatives) to expect, and how to account for that.
	Address	University of South Florida; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign
	Corporate Author				Thesis
	Publisher	Massey Unversity	Place of Publication	Palmerston North, New Zealand	Editor	Thomas J. Huggins, V.L.
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-0-473-66845-7	Medium
	Track	Social Media for Disaster Response	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2492
Share this record to Facebook



	Author	Matt Wolff
	Title	Unsupervised methods for detecting a malicious insider			Type	Conference Article
	Year	2010	Publication	ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings	Abbreviated Journal	ISCRAM 2010
	Volume		Issue		Pages
	Keywords	Information systems; Natural language processing systems; Network security; Unsupervised learning; Insider Threat; Malicious insiders; Masquerade attacks; Supervised algorithm; Unsupervised algorithms; Unsupervised method; User masquerades; Algorithms
	Abstract	One way a malicious insider can attack a network is by masquerading as a different user. Various algorithms have been proposed in an effort to detect when a user masquerade attack has occurred. In this paper, two unsupervised algorithms are proposed with the intended goal of detecting user masquerade attacks. The effectiveness of these two unsupervised algorithms are then compared against supervised algorithms.
	Address	University of Hawaii, United States
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Seattle, WA	Editor	S. French, B. Tomaszewski, C. Zobel
	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	Special Session: Information Credibility, Trust, Privacy and Security in Information Systems for Emergency Management	Expedition		Conference	7th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1097
Share this record to Facebook



	Author	Siska Fitrianie; Leon J.M. Rothkrantz
	Title	An automated crisis online dispatcher			Type	Conference Article
	Year	2007	Publication	Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers	Abbreviated Journal	ISCRAM 2007
	Volume		Issue		Pages	525-536
	Keywords	Human computer interaction; Natural language processing systems; Speech processing; Crisis management; Crisis situations; Dialogue systems; Emotion recognition; Emotional state; Human-computer dialogues; NAtural language processing; Natural user interactions; Behavioral research
	Abstract	An experimental automated dialogue system that plays the role of a crisis hotline dispatcher is currently developed. Besides controlling the communication flow, this system is able to retrieve information about crisis situations from user's input. It offers a natural user interaction by the ability to perceive and respond to human emotions. The system has an emotion recognizer that is able to recognize the emotional loading from user's linguistic content. The recognizer uses a database that contains selected keywords on a 2D “arousal” and “valence” scale. The output of the system provides not only the information about the user's emotional state but also an indication of the urgency of his/her information regarding to crisis. The dialogue system is able to start a user friendly dialogue, taking care of the content, context and emotional loading of user's utterances.
	Address	Man-Machine Interaction Group, Delft University of Technology, Netherlands
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Delft	Editor	B. Van de Walle, P. Burghardt, K. Nieuwenhuis
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9789054874171; 9789090218717	Medium
	Track	EMOT	Expedition		Conference	4th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	495
Share this record to Facebook



	Author	Starr Roxanne Hiltz; Linda Plotnick
	Title	Dealing with information overload when using social media for emergency management: Emerging solutions			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	823-827
	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
	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.
	Address	NJIT, Newark NJ, United States; Jacksonville State U., AL, 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	583
Share this record to Facebook



	Author	Susanne Jul
	Title	Who's really on first? A domain-level user, task and context analysis for response technology			Type	Conference Article
	Year	2007	Publication	Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers	Abbreviated Journal	ISCRAM 2007
	Volume		Issue		Pages	139-148
	Keywords	Design; Disasters; Management; Natural language processing systems; User interfaces; Crisis; Domain analysis; Emergency; Requirements analysis; Response; User interface designs; Emergency services
	Abstract	This paper presents a domain-level user, task and context analysis for response technology, based on sociological studies of disaster and disaster response. The analysis examines three dimensions of disaster-scale, kind and anticipability-that have been linked to differences in response characteristics, including differences in individual and organizational responders and behaviors. It yields a number of implications for design, and reveals five domain-specific design requirements. It also offers systematic characterizations of users, tasks and contexts of response technology, that begin to structure the user interface design space, laying a foundation for a theory of design. This work provides a conceptual basis to help researchers and designers recognize and address possible limitations of design decisions, leading, ultimately, to more usable and effective response technologies.
	Address	Pacific Disaster Center, United States
	Corporate Author				Thesis
	Publisher	Information Systems for Crisis Response and Management, ISCRAM	Place of Publication	Delft	Editor	B. Van de Walle, P. Burghardt, K. Nieuwenhuis
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9789054874171; 9789090218717	Medium
	Track	HCIS	Expedition		Conference	4th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	642
Share this record to Facebook



	Author	Xiaojing Guo; Xinzhi Wang; Luyao Kou; Hui Zhang
	Title	A Question Answering System Applied to Disasters			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	2-16
	Keywords	Emergency Management, Disaster, Natural Language Processing, Deep Learning
	Abstract	In emergency management, identifying disaster information accurately and promptly out of numerous documents like news articles, announcements, and reports is important for decision makers to accomplish their mission efficiently. This paper studies the application of the question answering system which can automatically locate answers in the documents by natural language processing to improve the efficiency and accuracy of disaster knowledge extraction. Firstly, an improved question answering model was constructed based on the advantages of the existing neural network models. Secondly, the English question answering dataset pertinent to disasters and the Chinese question answering dataset were constructed. Finally, the improved neural network model was trained on the datasets and tested by calculating the F1 and EM scores which indicated that a higher question answering accuracy was achieved. The improved system has a deeper understanding of the semantic information and can be used to construct the disaster knowledge graph.
	Address	Institute of Public Safety Research, Tsinghua University; School of Computer Engineering and Science, Shanghai University; Institute of Public Safety Research, Tsinghua University; Institute of Public Safety Research, Tsinghua University
	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	AI and Intelligent Systems for Crises and Risks	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	gxj19@mails.tsinghua.edu.cn			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2308
Share this record to Facebook



	Author	Yitong Li; Duoduo Liao; Jundong Li; Wenying Ji
	Title	Automated Generation of Disaster Response Networks through Information Extraction			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	431-438
	Keywords	Disaster response, Stakeholder collaboration, Natural language processing, Network generation
	Abstract	Following a disaster, maintaining and restoring community lifelines require collective efforts from various stakeholders. Aiming at reducing the efforts associated with generating stakeholder collaboration networks (SCNs), this research proposes a systematic approach for reliably extracting stakeholder collaboration information from texts and automatically generating SCNs. In the proposed approach, stakeholders and their interactions are automatically extracted from texts through a natural language processing technique--Named Entity Recognition. Once extracted, the collaboration information is stored into structured datasets to automate the generation of SCNs. A case study on stakeholder collaboration in response to Hurricane Harvey is used to demonstrate the feasibility and applicability of the proposed approach. Overall, the proposed approach achieves the reliable and automated generation of SCNs from texts, which largely reduces practitioners' interpretation loads and eases the data collection process.
	Address	George Mason University; George Mason University; University of Virginia; George Mason University
	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	Enhancing Resilient Response in Inter-organizational Contexts	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	wji2@gmu.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2344
Share this record to Facebook