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	Author	Alan Aipe; Asif Ekbal; Mukuntha NS; Sadao Kurohashi
	Title	Linguistic Feature Assisted Deep Learning Approach towards Multi-label Classification of Crisis Related Tweets			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	705-717
	Keywords	Deep learning, Multi-label classification, Social media, Crisis response
	Abstract	Micro-blogging site like Twitter, over the last decade, has evolved into a proactive communication channel during mass convergence and emergency events, especially in crisis stricken scenarios. Extracting multiple levels of information associated with the overwhelming amount of social media data generated during such situations remains a great challenge to disaster-affected communities and professional emergency responders. These valuable data, segregated into different informative categories, can be leveraged by the government agencies, humanitarian communities as well as citizens to bring about faster response in areas of necessity. In this paper, we address the above scenario by developing a deep Convolutional Neural Network (CNN) for multi-label classification of crisis related tweets.We augment deep CNN by several linguistic features extracted from Tweet, and investigate their usage in classification. Evaluation on a benchmark dataset show that our proposed approach attains the state-of-the-art performance.
	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	2144
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	Author	Anna Kruspe; Jens Kersten; Friederike Klan
	Title	Detecting event-related tweets by example using few-shot models			Type	Conference Article
	Year	2019	Publication	Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management	Abbreviated Journal	Iscram 2019
	Volume		Issue		Pages
	Keywords	Social media, Twitter, Relevance, Keywords, Hashtags, Few-shot models, One-class classification
	Abstract	Social media sources can be helpful in crisis situations, but discovering relevant messages is not trivial. Methods have so far focused on universal detection models for all kinds of crises or for certain crisis types (e.g. floods). Event-specific models could implement a more focused search area, but collecting data and training new models for a crisis that is already in progress is costly and may take too much time for a prompt response. As a compromise, manually collecting a small amount of example messages is feasible. Few-shot models can generalize to unseen classes with such a small handful of examples, and do not need be trained anew for each event. We show how these models can be used to detect crisis-relevant tweets during new events with just 10 to 100 examples and counterexamples. We also propose a new type of few-shot model that does not require counterexamples.
	Address	German Aerospace Center (DLR), Germany
	Corporate Author				Thesis
	Publisher	Iscram	Place of Publication	Valencia, Spain	Editor	Franco, Z.; González, J.J.; Canós, J.H.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-84-09-10498-7	Medium
	Track	T8- Social Media in Crises and Conflicts	Expedition		Conference	16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
	Notes				Approved	no
	Call Number				Serial	1911
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	Author	Anne-Francoise Rutkowski; Willem Van Groenendaal; Bartel A. Van De Walle; Jan Pol
	Title	Decision support technology to support risk analysis and disaster recovery plan formulation: Towards IT and business continuity			Type	Conference Article
	Year	2004	Publication	Proceedings of ISCRAM 2004 – 1st International Workshop on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2004
	Volume		Issue		Pages	127-132
	Keywords	Decision support systems; Disasters; Groupware; Information management; Information systems; Mobile telecommunication systems; Risk analysis; Business continuity; Business continuity plans; Disaster recovery plan; Economic decision model; Group support systems; Multi-national companies; Quantitative classifications; Recovery planning; Recovery
	Abstract	The paper presents a four-phase action research project that was (and still is) conducted at the department of Information Management Customer Support and Operations (IM\CS&O) of a large multi-national company. The department is in charge of ICT-service continuity and has to produce ICT recovery plans that are integrated with the organization's overall Business Continuity plan. Interviews, Group Support System (GSS) technologies as well as a risk survey have been used to gather information and identify risks and threats. A systematic quantitative classification, measuring the impact of loss of ICT services on the company's business processes in terms of cost and risk will allow in the near future to utilize an economic decision model to prioritize the core activities of training and implementation of a recovery disaster plan. The research has made clear to the involved protagonists the necessity to share information, to develop awareness, and to formulate a shared recovery disaster plan to ensure ICT/business continuity and/or recovery when ICT disruptions occurs. Â© Proceedings ISCRAM 2004.
	Address	Department of Information Systems and Management, Tilburg University, Tilburg, Netherlands; Philips Medical Systems, Best, Netherlands
	Corporate Author				Thesis
	Publisher	Royal Flemish Academy of Belgium	Place of Publication	Brussels	Editor	B. Van de Walle, B. Carle
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9076971080	Medium
	Track	Emergency Response Stakeholders and Cooperation	Expedition		Conference	1st International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	197
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	Author	Benjamin Herfort; Melanie Eckle; João Porto de Albuquerque
	Title	Being Specific about Geographic Information Crowdsourcing: A Typology and Analysis of the Missing Maps Project in South Kivu			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Crowdsourcing; Classification; Digitization; Conflation; OpenStreetMap
	Abstract	Recent development in disaster management and humanitarian aid is shaped by the rise of new information sources such as social media or volunteered geographic information. As these show great potential, making sense out of the new geographical datasets is a field of important scientific research. Therefore, this paper attempts to develop a typology of geographical information crowdsourcing. Furthermore, we use this typology to frame existing crowdsourcing projects and to further point out the potential of different kinds of crowdsourcing for disaster management and humanitarian aid. In order to exemplify its practical usage and value, we apply the typology to analyze the crowdsourcing methods utilized by the members of the Missing Maps project developed in South Kivu.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3438	ISBN	978-84-608-7984-59	Medium
	Track	Geospatial Data and Geographical Information Science	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1378
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	Author	Christoph Endres; Andreas Wurz; Marcus Hoffmann; Alexander Behring
	Title	A task-based messaging approach to facilitate staff work			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	Communication; Hardware; Assisting tools; Classification scheme; Collaboration; Incident Command (IC); Messages; State of the art; Task-based; User study; Information systems
	Abstract	A central part of the work in Incident Commands (ICs) deals with handling messages that contain relevant information. Classification schemes for messages can be exploited by command staff and assisting tools to support this work, given that a common understanding of the scheme is shared among participants. We present user studies on two such classifications, which imply some disagreement among participants. Interpretations of the studies and a revised scheme are presented. All users in our studies are highly trained experts and represent the state of the art in german IC work.
	Address	DFKI GmbH, Germany; Fire Department, Cologne, Germany; Fraunhofer IGD, Germany; TU, Darmstadt, Germany
	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	Poster Session	Expedition		Conference	7th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	474
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	Author	Cornelia Caragea; Adrian Silvescu; Andrea Tapia
	Title	Identifying Informative Messages in Disasters using Convolutional Neural Networks			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Informative Tweets Classification; Disaster Events; Convolutional Neural Networks
	Abstract	Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples? ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the ?bag of words? and n-grams as features on several datasets of messages from flooding events.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1397
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	Author	Cornelia Caragea; Anna Squicciarini; Sam Stehle; Kishore Neppalli; Andrea H. Tapia
	Title	Mapping moods: Geo-mapped sentiment analysis during hurricane sandy			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	642-651
	Keywords	Data mining; Disasters; Hurricanes; Information systems; Disaster-related geo-tagged tweets; Online reviews; Online social networkings; Sentiment analysis; Sentiment classification; Social networking sites; Social networking (online)
	Abstract	Sentiment analysis has been widely researched in the domain of online review sites with the aim of generating summarized opinions of product users about different aspects of the products. However, there has been little work focusing on identifying the polarity of sentiments expressed by users during disaster events. Identifying sentiments expressed by users in an online social networking site can help understand the dynamics of the network, e.g., the main users' concerns, panics, and the emotional impacts of interactions among members. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. In this work, we perform sentiment classification of user posts in Twitter during the Hurricane Sandy and visualize these sentiments on a geographical map centered around the hurricane. We show how users' sentiments change according not only to users' locations, but also based on the distance from the disaster.
	Address	Computer Science and Engineering, University of North Texas, Denton, TX-76203, United States; Information Sciences and Technology, Pennsylvania State University, University Park, PA-16801, United States
	Corporate Author				Thesis
	Publisher	The Pennsylvania State University	Place of Publication	University Park, PA	Editor	S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780692211946	Medium
	Track	Social Media in Crisis Response and Management	Expedition		Conference	11th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	372
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	Author	Dat T. Nguyen; Firoj Alam; Ferda Ofli; Muhammad Imran
	Title	Automatic Image Filtering on Social Networks Using Deep Learning and Perceptual Hashing During Crises			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	499-511
	Keywords	social media; image processing; supervised classification; disaster management
	Abstract	The extensive use of social media platforms, especially during disasters, creates unique opportunities for humanitarian organizations to gain situational awareness and launch relief operations accordingly. In addition to the textual content, people post overwhelming amounts of imagery data on social networks within minutes of a disaster hit. Studies point to the importance of this online imagery content for emergency response. Despite recent advances in the computer vision field, automatic processing of the crisis-related social media imagery data remains a challenging task. It is because a majority of which consists of redundant and irrelevant content. In this paper, we present an image processing pipeline that comprises de-duplication and relevancy filtering mechanisms to collect and filter social media image content in real-time during a crisis event. Results obtained from extensive experiments on real-world crisis datasets demonstrate the significance of the proposed pipeline for optimal utilization of both human and machine computing resources.
	Address	Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar
	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	2038
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	Author	Firoj Alam; Ferda Ofli; Muhammad Imran
	Title	CrisisDPS: Crisis Data Processing Services			Type	Conference Article
	Year	2019	Publication	Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management	Abbreviated Journal	Iscram 2019
	Volume		Issue		Pages
	Keywords	Social media, humanitarian data processing, text classification, application programming interfaces, data processing services
	Abstract	Over the last few years, extensive research has been conducted to develop technologies to support humanitarian aid tasks. However, many technologies are still limited as they require both manual and automatic approaches, and more importantly, are not ready to be integrated into the disaster response workflows. To tackle this limitation, we develop automatic data processing services that are freely and publicly available, and made to be simple, efficient, and accessible to non-experts. Our services take textual messages (e.g., tweets, Facebook posts, SMS) as input to determine (i) which disaster type the message belongs to, (ii) whether it is informative or not, and (iii) what type of humanitarian information it conveys. We built our services upon machine learning classifiers that are obtained from large-scale comparative experiments utilizing both classical and deep learning algorithms. Our services outperform state-of-the-art publicly available tools in terms of classification accuracy.
	Address	Qatar Computing Research Institute, Qatar
	Corporate Author				Thesis
	Publisher	Iscram	Place of Publication	Valencia, Spain	Editor	Franco, Z.; González, J.J.; Canós, J.H.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-84-09-10498-7	Medium
	Track	T8- Social Media in Crises and Conflicts	Expedition		Conference	16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
	Notes				Approved	no
	Call Number				Serial	1891
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	Author	Firoj Alam; Ferda Ofli; Muhammad Imran; Michael Aupetit
	Title	A Twitter Tale of Three Hurricanes: Harvey, Irma, and Maria			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	553-572
	Keywords	social media, artificial intelligence, image processing, supervised classification, disaster management
	Abstract	People increasingly use microblogging platforms such as Twitter during natural disasters and emergencies. Research studies have revealed the usefulness of the data available on Twitter for several disaster response tasks. However, making sense of social media data is a challenging task due to several reasons such as limitations of available tools to analyze high-volume and high-velocity data streams. This work presents an extensive multidimensional analysis of textual and multimedia content from millions of tweets shared on Twitter during the three disaster events. Specifically, we employ various Artificial Intelligence techniques from Natural Language Processing and Computer Vision fields, which exploit different machine learning algorithms to process the data generated during the disaster events. Our study reveals the distributions of various types of useful information that can inform crisis managers and responders as well as facilitate the development of future automated systems for disaster management.
	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	2131
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	Author	Hafiz Budi Firmansyah; Jesus Cerquides; Jose Luis Fernandez-Marquez
	Title	Ensemble Learning for the Classification of Social Media Data in Disaster Response			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	710-718
	Keywords	Ensemble learning; image classification; social media; disaster response
	Abstract	Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment.
	Address	Citizen Cyberlab, CUI, University of Geneva, Switzerland; Citizen Cyberlab, CUI, University of Geneva, Switzerland; IIIA-CSIC, Barcelona, Spain
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2450
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	Author	Hemant Purohit; Jennifer Chan
	Title	Classifying User Types on Social Media to inform Who-What-Where Coordination during Crisis Response			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	656-665
	Keywords	User Classification, Social Media, Crisis Coordination, Organization, Organization-affiliated
	Abstract	Timely information is essential for better dynamic situational awareness, which leads to efficient resource planning, coordination, and action. However, given the scale and outreach of social media�a key information sharing platform during crises, diverse types of users participate in discussions during crises, which affect the vetting of information for dynamic situational awareness and response coordination activities. In this paper, we present a user analysis on Twitter during crises for three major user types�Organization, Organizationaffiliated (a person�s self-identifying affiliation with an organization in his/her profile), and Non-affiliated (person not identifying any affiliation), by first classifying users and then presenting their communication patterns during two recent crises. Our analysis shows distinctive patterns of the three user types for participation and communication on social media during crises. Such a user-centric approach to study information sharing during crisis events can act as a precursor to deeper domain-driven content analysis for response agencies.
	Address
	Corporate Author				Thesis
	Publisher	Iscram	Place of Publication	Albi, France	Editor	Tina Comes, F.B., Chihab Hanachi, Matthieu Lauras, Aurélie Montarnal, eds
	Language	Englisg	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	ISCRAM @ idladmin @			Serial	2200
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	Author	Hongmin Li; Doina Caragea; Cornelia Caragea
	Title	Combining Self-training with Deep Learning for Disaster Tweet Classification			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	719-730
	Keywords	Domain Adaptation, Self-training, Crisis Tweets Classification, BERT, CNN
	Abstract	Significant progress has been made towards automated classification of disaster or crisis related tweets using machine learning approaches. Deep learning models, such as Convolutional Neural Networks (CNN), domain adaptation approaches based on self-training, and approaches based on pre-trained language models, such as BERT, have been proposed and used independently for disaster tweet classification. In this paper, we propose to combine self-training with CNN and BERT models, respectively, to improve the performance on the task of identifying crisis related tweets in a target disaster where labeled data is assumed to be unavailable, while unlabeled data is available. We evaluate the resulting self-training models on three crisis tweet collections and find that: 1) the pre-trained language model BERTweet is better than the standard BERT model, when fine-tuned for downstream crisis tweets classification; 2) self-training can help improve the performance of the CNN and BERTweet models for larger unlabeled target datasets, but not for smaller datasets.
	Address	Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University; Department of Computer Science, University of Illinois at Chicago
	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	hongminli@ksu.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2367
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	Author	Hongmin Li; Doina Caragea; Cornelia Caragea
	Title	Towards Practical Usage of a Domain Adaptation Algorithm in the Early Hours of a Disaster			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	692-704
	Keywords	Twitter; Domain adaptation; Disaster; Classification
	Abstract	Many machine learning techniques have been proposed to reduce the information overload in social media data during an emergency situation. Among such techniques, domain adaptation approaches present greater potential as compared to supervised algorithms because they don't require labeled data from the current disaster for training. However, the use of domain adaptation approaches in practice is sporadic at best. One reason is that domain adaptation algorithms have parameters that need to be tuned using labeled data from the target disaster, which is presumably not available. To address this limitation, we perform a study on one domain adaptation approach with the goal of understanding how much source data is needed to obtain good performance in a practical situation, and what parameter values of the approach give overall good performance. The results of our study provide useful insights into the practical application of domain adaptation algorithms in real crisis situations.
	Address	Kansas State University; University of North Texas
	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	ISCRAM @ idladmin @			Serial	2057
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	Author	Hongmin Li; Nicolais Guevara; Nic Herndon; Doina Caragea; Kishore Neppalli; Cornelia Caragea; Anna Squicciarini; Andrea H. Tapia
	Title	Twitter Mining for Disaster Response: A Domain Adaptation Approach			Type	Conference Article
	Year	2015	Publication	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2015
	Volume		Issue		Pages
	Keywords	Disaster Response; domain adaptation; tweet classification
	Abstract	Microblogging data such as Twitter data contains valuable information that has the potential to help improve the speed, quality, and efficiency of disaster response. Machine learning can help with this by prioritizing the tweets with respect to various classification criteria. However, supervised learning algorithms require labeled data to learn accurate classifiers. Unfortunately, for a new disaster, labeled tweets are not easily available, while they are usually available for previous disasters. Furthermore, unlabeled tweets from the current disaster are accumulating fast. We study the usefulness of labeled data from a prior source disaster, together with unlabeled data from the current target disaster to learn domain adaptation classifiers for the target. Experimental results suggest that, for some tasks, source data itself can be useful for classifying target data. However, for tasks specific to a particular disaster, domain adaptation approaches that use target unlabeled data in addition to source labeled data are superior.
	Address
	Corporate Author				Thesis
	Publisher	University of Agder (UiA)	Place of Publication	Kristiansand, Norway	Editor	L. Palen; M. Buscher; T. Comes; A. Hughes
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9788271177881	Medium
	Track	Social Media Studies	Expedition		Conference	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	yes
	Call Number				Serial	1234
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	Author	Hongmin Li; Xukun Li; Doina Caragea; Cornelia Caragea
	Title	Comparison of Word Embeddings and Sentence Encodings for Generalized Representations in Crisis Tweet Classifications			Type	Conference Article
	Year	2018	Publication	Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific.	Abbreviated Journal	Iscram Ap 2018
	Volume		Issue		Pages	480-493
	Keywords	Word Embeddings, Sentence Encodings, Reduced Tweet Representation, Crisis Tweet Classification
	Abstract	Many machine learning and natural language processing techniques, including supervised and domain adaptation algorithms, have been proposed and studied in the context of filtering crisis tweets. However, applying these approaches in real-time is still challenging because of time-critical requirements of emergency response operations and also diversities and unique characteristics of emergency events. In this paper, we explore the idea of building “generalized” classifiers for filtering crisis tweets that can be pre-trained, and are thus ready to use in real-time, while generalizing well on future disasters/crises data. We propose to achieve this using simple feature based adaptation with tweet representations based on word embeddings and also sentence-level embeddings, representations which do not rely on unlabeled data to achieve domain adaptations and can be easily implemented. Given that there are different types of word/sentence embeddings that are widely used, we propose to compare them to get a general idea about which type works better with crisis tweets classification tasks. Our experimental results show that GloVe embeddings in general work better with the datasets used in our evaluation, and that the supervised algorithms used in our experiments benefit from GloVe embeddings trained specifically on crisis data. Furthermore, our experimental results show that following GloVe, the sentence embeddings have great potential in crisis tweet tasks.
	Address	Kansas State University; Kansas State University; Kansas State University; Kansas State University
	Corporate Author				Thesis
	Publisher	Massey Univeristy	Place of Publication	Albany, Auckland, New Zealand	Editor	Kristin Stock; Deborah Bunker
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN		ISBN		Medium
	Track	Social Media and Community Engagement Supporting Resilience Building	Expedition		Conference
	Notes				Approved	no
	Call Number				Serial	1689
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	Author	Jens Kersten; Jan Bongard; Friederike Klan
	Title	Gaussian Processes for One-class and Binary Classification of Crisis-related Tweets			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	664-673
	Keywords	Gaussian Process; One-class Classification; Twitter; Overload Reduction; Crisis Informatics
	Abstract	Overload reduction is essential to exploit Twitter text data for crisis management. Often used pre-trained machine learning models require training data for both, crisis-related and off-topic content. However, this task can also be formulated as a one-class classification problem in which labeled off-topic samples are not required. Gaussian processes (GPs) have great potential in both, binary and one-class settings and are therefore investigated in this work. Deep kernel learning combines the representative power of text embeddings with the Bayesian formalism of GPs. Motivated by this, we investigate the potential of deep kernel models for the task of classifying crisis-related tweet texts with special emphasis on cross-event applications. Compared to standard binary neural networks, first experiments with one-class GP models reveal a great potential for realistic scenarios, offering a fast and flexible approach for interactive model training without requiring off-topic training samples and comprehensive expert knowledge (only two model parameters involved).
	Address	German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2446
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	Author	Jeremy Diaz; Lise St. Denis; Maxwell B. Joseph; Kylen Solvik; Jennifer K. Balch
	Title	Classifying Twitter Users for Disaster Response: A Highly Multimodal or Simple Approach?			Type	Conference Article
	Year	2020	Publication	ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2020
	Volume		Issue		Pages	774-789
	Keywords	User Classification, Disaster Response, Twitter, Model Comparison, Multimodal Deep Learning.
	Abstract	We report on the development of a classifier to identify Twitter users contributing first-hand information during a disaster. Identifying such users helps social media monitoring teams identify critical information that might otherwise slip through the cracks. A parallel study (St. Denis et al., 2020) demonstrates that Twitter user filtering creates an information-rich stream of content, but the best way to approach this task is unexplored. A user's profile contains many different “modalities” of data, including numbers, text, and images. To integrate these different data types, we constructed a multimodal neural network that combines the loss function of all modalities, and we compared the results to many individual unimodal models and a decision-level fusion approach. Analysis of the results suggests that unimodal models acting on Twitter users' recent tweets are sufficient for accurate classification. We demonstrate promising classification of Twitter users for crisis response with methods that are (1) easy to implement and (2) quick to both optimize and infer.
	Address	Institute for Computational and Data Sciences, The Penn State University Department of Geography, The Penn State University; CIRES, Earth Lab, University of Colorado, Boulder; CIRES, Earth Lab, University of Colorado, Boulder; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Amanda Hughes; Fiona McNeill; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-27-69	ISBN	2411-3455	Medium
	Track	Social Media for Disaster Response and Resilie	Expedition		Conference	17th International Conference on Information Systems for Crisis Response and Management
	Notes	jad6655@psu.edu			Approved	no
	Call Number				Serial	2270
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	Author	Koki Asami; Shono Fujita; Kei Hiroi; Michinori Hatayama
	Title	Data Augmentation with Synthesized Damaged Roof Images Generated by GAN			Type	Conference Article
	Year	2022	Publication	ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2022
	Volume		Issue		Pages	256-265
	Keywords	disaster response; generative adversarial networks; data augmentation; damage classification
	Abstract	The lack of availability of large and diverse labeled datasets is one of the most critical issues in the use of machine learning in disaster prevention. Natural disasters are rare occurrences, which makes it difficult to collect sufficient disaster data for training machine learning models. The imbalance between disaster and non-disaster data affects the performance of machine learning algorithms. This study proposes a generative adversarial network (GAN)- based data augmentation, which generates realistic synthesized disaster data to expand the disaster dataset. The effect of the proposed augmentation was validated in the roof damage rate classification task, which improved the recall score by 11.4% on average for classes with small raw data and a high ratio of conventional augmentations such as rotation of image, and the overall recall score improved by 3.9%.
	Address	Kyoto University; Kyoto University; Kyoto University; Kyoto University
	Corporate Author				Thesis
	Publisher		Place of Publication	Tarbes, France	Editor	Rob Grace; Hossein Baharmand
	Language	English	Summary Language		Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-82-8427-099-9	Medium
	Track	AI and Intelligent Systems for Crises and Risks	Expedition		Conference
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2415
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	Author	Leon J. M. Rothkrantz; Siska Fitrianie
	Title	Bayesian Classification of Disaster Events on the Basis of Icon Messages of Observers			Type	Conference Article
	Year	2015	Publication	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2015
	Volume		Issue		Pages
	Keywords	Bayesian reasoning; classification of disaster events; crisis ontology; Icon-based language
	Abstract	During major disaster events, human operators in a crisis center will be overloaded with under-stress a flood of phone calls. As an increasing number of people in and around big cities do not master the native language, the need for automated systems that automatically process the context and content of information about disaster situations from the communicated messages becomes apparent. To support language-independent communication and to reduce the ambiguity and multitude semantics, we developed an icon-based reporting observation system. Contrast to previous approaches of such a system, we link icon messages to disaster events without using Natural Language Processing. We developed a dedicated set of icons related to the context and characteristic features of disaster events. The developed system is able to compute the probability of the appearance of possible disaster events using Bayesian reasoning. In this paper, we present the reporting system, the developed icons, the Bayesian model, and the results of two experiments.
	Address
	Corporate Author				Thesis
	Publisher	University of Agder (UiA)	Place of Publication	Kristiansand, Norway	Editor	L. Palen; M. Buscher; T. Comes; A. Hughes
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9788271177881	Medium
	Track	Human Centred Design and Evaluation	Expedition		Conference	ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	yes
	Call Number				Serial	1219
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	Author	Liuqing Li; Edward A. Fox
	Title	Disaster Response Patterns across Different User Groups on Twitter: A Case Study during Hurricane Dorian			Type	Conference Article
	Year	2020	Publication	ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	Iscram 2020
	Volume		Issue		Pages	838-848
	Keywords	Hurricane, Response, Pattern, User Classification, Twitter
	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.
	Address	Department of Computer Science, Virginia Tech; Department of Computer Science, Virginia Tech;
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Amanda Hughes; Fiona McNeill; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-27-74	ISBN	2411-3460	Medium
	Track	Social Media for Disaster Response and Resilie	Expedition		Conference	17th International Conference on Information Systems for Crisis Response and Management
	Notes	liuqing@vt.edu			Approved	no
	Call Number				Serial	2275
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	Author	Liuqing Li; Edward A. Fox
	Title	Understanding patterns and mood changes through tweets about disasters			Type	Conference Article
	Year	2019	Publication	Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management	Abbreviated Journal	Iscram 2019
	Volume		Issue		Pages
	Keywords	Disaster, Pattern, User Classification, Mood Detection, Twitter
	Abstract	We analyzed a sample of large tweet collections gathered since 2011, to expand understanding about tweeting patterns and emotional responses of different types of tweeters regarding disasters. We selected three examples for each of four disaster types: school shooting, bombing, earthquake, and hurricane. For each collection, we deployed our novel model TwiRole for user classification, and an existing deep learning model for mood detection. We found differences in the daily tweet count patterns, between the different types of events. Likewise, there were different average scores and patterns of moods (fear, sadness, surprise), both between types of events, and between events of the same type. Further, regarding surprise and fear, there were differences among roles of tweeters. These results suggest the value of further exploration as well as hypothesis testing with our hundreds of event and trend related tweet collections, considering indications in those that reflect emotional responses to disasters.
	Address	Virginia Tech, United States of America
	Corporate Author				Thesis
	Publisher	Iscram	Place of Publication	Valencia, Spain	Editor	Franco, Z.; González, J.J.; Canós, J.H.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	978-84-09-10498-7	Medium
	Track	T8- Social Media in Crises and Conflicts	Expedition		Conference	16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
	Notes				Approved	no
	Call Number				Serial	1863
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	Author	Long, Z.; McCreadiem, R.; Imran, M.
	Title	CrisisViT: A Robust Vision Transformer for Crisis Image Classification			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	309-319
	Keywords	Social Media Classification; Crisis Management; Deep Learning; Vision Transformers; Supervised Learning
	Abstract	In times of emergency, crisis response agencies need to quickly and accurately assess the situation on the ground in order to deploy relevant services and resources. However, authorities often have to make decisions based on limited information, as data on affected regions can be scarce until local response services can provide first-hand reports. Fortunately, the widespread availability of smartphones with high-quality cameras has made citizen journalism through social media a valuable source of information for crisis responders. However, analyzing the large volume of images posted by citizens requires more time and effort than is typically available. To address this issue, this paper proposes the use of state-of-the-art deep neural models for automatic image classification/tagging, specifically by adapting transformer-based architectures for crisis image classification (CrisisViT). We leverage the new Incidents1M crisis image dataset to develop a range of new transformer-based image classification models. Through experimentation over the standard Crisis image benchmark dataset, we demonstrate that the CrisisViT models significantly outperform previous approaches in emergency type, image relevance, humanitarian category, and damage severity classification. Additionally, we show that the new Incidents1M dataset can further augment the CrisisViT models resulting in an additional 1.25% absolute accuracy gain.
	Address	University of Glasgow
	Corporate Author				Thesis
	Publisher	University of Nebraska at Omaha	Place of Publication	Omaha, USA	Editor	Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi
	Language	English	Summary Language		Original Title
	Series Editor	Hosssein Baharmand	Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition	1
	ISSN		ISBN		Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/SDSM9194			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2528
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	Author	Muhammad Imran; Carlos Castillo; Jesse Lucas; Patrick Meier; Jakob Rogstadius
	Title	Coordinating human and machine intelligence to classify microblog communications in crises			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	712-721
	Keywords	Information systems; Classification accuracy; Disaster response; Human intelligence; Large-scale datum; Machine computations; Machine intelligence; Real-world datasets; Supervised classifiers; Artificial intelligence
	Abstract	An emerging paradigm for the processing of data streams involves human and machine computation working together, allowing human intelligence to process large-scale data. We apply this approach to the classification of crisis-related messages in microblog streams. We begin by describing the platform AIDR (Artificial Intelligence for Disaster Response), which collects human annotations over time to create and maintain automatic supervised classifiers for social media messages. Next, we study two significant challenges in its design: (1) identifying which elements must be labeled by humans, and (2) determining when to ask for such annotations to be done. The first challenge is selecting the items to be labeled by crowd sourcing workers to maximize the productivity of their work. The second challenge is to schedule the work in order to reliably maintain high classification accuracy over time. We provide and validate answers to these challenges by extensive experimentation on real world datasets.
	Address	Qatar Computing Research Institute, Qatar; University of Madeira, Portugal
	Corporate Author				Thesis
	Publisher	The Pennsylvania State University	Place of Publication	University Park, PA	Editor	S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780692211946	Medium
	Track	Social Media in Crisis Response and Management	Expedition		Conference	11th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	612
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	Author	Muhammad Imran; Prasenjit Mitra; Jaideep Srivastava
	Title	Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Social Media; Tweets Classification; Domain Adaptation
	Abstract	Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1396
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