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	Author	Zou, H.P.; Caragea, C.; Zhou, Y.; Caragea, D.
	Title	Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	385-395
	Keywords	Crisis Tweet Classification; Semi-Supervised Few-Shot Learning; Pseudo-Labeling; TextMixUp.
	Abstract	The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results.
	Address	University of Illinois Chicago; Kansas State University
	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	2411-3387	ISBN	979-8-218-21749-5	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/FWXE4933			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2533
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	Author	Zoha Sheikh; Hira Masood; Sharifullah Khan; Muhammad Imran
	Title	User-Assisted Information Extraction from Twitter During Emergencies			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	684-691
	Keywords	social media; disaster response; query expansion; supervised learning
	Abstract	Disasters and emergencies bring uncertain situations. People involved in such situations look for quick answers to their rapid queries. Moreover, humanitarian organizations look for situational awareness information to launch relief operations. Existing studies show the usefulness of social media content during crisis situations. However, despite advances in information retrieval and text processing techniques, access to relevant information on Twitter is still a challenging task. In this paper, we propose a novel approach to provide timely access to the relevant information on Twitter. Specifically, we employee Word2vec embeddings to expand initial users queries and based on a relevance feedback mechanism we retrieve relevant messages on Twitter in real-time. Initial experiments and user studies performed using a real world disaster dataset show the significance of the proposed approach.
	Address	National University of Sciences and Technology, Islamabad, Pakistan; Qatar Computing Research Institute, HBKU 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	2056
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	Author	Zijun Long; Richard Mccreadie
	Title	Automated Crisis Content Categorization for COVID-19 Tweet Streams			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	667-678
	Keywords	COVID-19, Tweet Classification, Crisis Management, Deep Learning
	Abstract	Social media platforms, like Twitter, are increasingly used by billions of people internationally to share information. As such, these platforms contain vast volumes of real-time multimedia content about the world, which could be invaluable for a range of tasks such as incident tracking, damage estimation during disasters, insurance risk estimation, and more. By mining this real-time data, there are substantial economic benefits, as well as opportunities to save lives. Currently, the COVID-19 pandemic is attacking societies at an unprecedented speed and scale, forming an important use-case for social media analysis. However, the amount of information during such crisis events is vast and information normally exists in unstructured and multiple formats, making manual analysis very time consuming. Hence, in this paper, we examine how to extract valuable information from tweets related to COVID-19 automatically. For 12 geographical locations, we experiment with supervised approaches for labelling tweets into 7 crisis categories, as well as investigated automatic priority estimation, using both classical and deep learned approaches. Through evaluation using the TREC-IS 2020 COVID-19 datasets, we demonstrated that effective automatic labelling for this task is possible with an average of 61% F1 performance across crisis categories, while also analysing key factors that affect model performance and model generalizability across locations.
	Address	University of Glasgow; University of Glasgow
	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	2452593L@student.gla.ac.uk			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2363
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	Author	Zijun Long; Richard McCreadie
	Title	Is Multi-Modal Data Key for Crisis Content Categorization on Social Media?			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	1068-1080
	Keywords	Social Media Classification; Multi-modal Learning; Crisis Management; Deep Learning, BERT; Supervised Learning
	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.
	Address	University of Glasgow; University of Glasgow
	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	2472
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	Author	Zeno Franco; Syed Ahmed; Craig E. Kuziemsky; Paul A. Biedrzycki; Anne Kissack
	Title	Using social network analysis to explore issues of latency, connectivity, interoperability & sustainability in community disaster response			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	896-900
	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
	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.
	Address	Medical College of Wisconsin, United States; U. Ottawa, Canada; City of Milwaukee Public Health Department, United Kingdom
	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	504
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	Author	Yuya Shibuya; Hideyuki Tanaka
	Title	Detecting Disaster Recovery Activities via Social Media Communication Topics			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, Topic modeling, Socio-economic recovery, Used-car demand, Housing demand.
	Abstract	Enhancing situational awareness by mining social media has been widely studied, but little work has been done focusing on recovery phases. To provide evidence to support the possibility of harnessing social media as a sensor of recovery activities, we examine the correlations between topic frequencies on Twitter and people?s socioeconomic recovery activities as reflected in the excess demand for used cars and housing, after the Great East Japan Earthquake and Tsunami of 2011. Our research suggests that people in the disaster-stricken area communicated more about recovery and disaster damages when they needed to purchase used cars, while the nonlocal population communicated more about going to and supporting the disaster-stricken area. On the other hand, regarding the excess demand for housing, when the local population of the disaster-stricken area started to resettle, they communicated their opinions more than in other periods about disaster-related situations.
	Address	The University of Tokyo, Japan
	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	1889
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	Author	Yulia Tyshchuk; William A. Wallace
	Title	The use of social media by local government in response to an extreme event: Del norte county, CA response to the 2011 Japan tsunami			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	802-811
	Keywords	Civil defense; Information systems; Risk management; Tsunamis; Affected area; Emergency management; Extreme events; Feed-back loop; First responders; Local government; Monitoring information; Social media; Disasters
	Abstract	Social media has become increasingly important for emergency management. One example is its current use by governmental organizations to disseminate emergency-relevant information. During disaster events, it is imperative for people in affected areas to obtain accurate information. People using social media make a conscious decision to trust, act on, propagate or disregard emergency-relevant information. However, local government, in general, has not developed agreed upon ways to use social media in emergencies. This study documents how emergency management was able to successfully partner with local media and utilize social media to develop important relationships with the affected community via social media in emergencies. The study demonstrates a way to successfully utilize social media during disaster events in several ways: by closing a feedback loop between first responders and the public, by monitoring information flow, and by providing regular updates to the public.
	Address	Rensselaer Polytechnic Institute, 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	1031
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	Author	Yuhong Li; Christopher Zobel
	Title	Small Businesses and Social Media Usage in the 2013 Colorado Floods			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; Small Business; Recovery; Disaster
	Abstract	The recovery of small businesses from a disaster is critical to community recovery. Such businesses can be extremely vulnerable to disasters, particularly because they often occupy a single location and have a localized customer base. Although social media is an effective platform for information dissemination, and has been extensively used in a disaster context, the way in which small businesses use social media in this context, and the effectiveness of those efforts, are still not well understood. With this in mind, this paper uses the 2013 floods along the Front Range in Colorado as a case study to help improve our understanding of how small businesses use social media in disaster situations. Characterizing the organizations' behavior involves using both qualitative and quantitative approaches, and the paper focuses on an initial qualitative analysis.
	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	1392
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	Author	Yudi Chen; Angel Umana; Chaowei Yang; Wenying Ji
	Title	Condition Sensing for Electricity Infrastructures in Disasters by Mining Public Topics from Social Media			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	598-608
	Keywords	social media, infrastructure resilience, human behaviors, disaster response
	Abstract	Timely and reliable sensing of infrastructure conditions is critical in disaster management for planning effective infrastructure restorations. Social media, a near real-time information source, has been widely used in the disaster domain for building timely, general situational awareness, such as urgent public needs and donations. However, the employment of social media for sensing electricity infrastructure conditions has yet been explored. This study aims to address the research gap to sense electricity infrastructure conditions through mining public topics from social media. To achieve this purpose, we proposed a systematic and customized approach wherein (1) electricity-related social media data is extracted by the classifier developed based on Bidirectional Encoder Representations from Transformers (BERT); and (2) public topics are modeled with unigrams, bigrams, and trigrams to incorporate the formulaic expressions of infrastructure conditions in social media. Electricity infrastructures in Florida impacted by Hurricane Irma are studied for illustration and demonstration. Results show that the proposed approach is capable of sensing the temporal evolutions and geographic differences of electricity infrastructure conditions.
	Address	George Mason University; George Mason University; George Mason University; 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	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	wji2@gmu.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2358
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	Author	Yu, X.; Chen, J.; Liu, J.
	Title	Examining the influence of social media on individual’s protective action taking during Covid-19 in China			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	295-308
	Keywords	Public Crisis; Social Mediated Crisis Communication Model; Risk Perception; Protective Action
	Abstract	In the context of COVID-19, this study utilizes the Social Mediated Crisis Communication Model (SMCC) and the Protective Action Decision Model (PADM) to investigate the relationship between social media users' protective actions and crisis information during public health crises in China. By constructing a structural equation model, this study aims to identify the influencing factors that affect social media users' personal’s cognitive, emotional, and behavioral reactions given crisis relevant information. Results findings are that warning information can significantly increase risk perception; emotional responses are not significantly affected by warning information and risk perception; risk perception has a negative impact on information gathering and sharing behavior; risk perception has a significant mediating effect on the relationship between information features and protective action.
	Address	University of International Business and Economics
	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	2411-3387	ISBN	979-8-218-21749-5	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/HPVH6600			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2527
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	Author	Yongzhong Sha; Jinsong Yan; Guoray Cai
	Title	Detecting public sentiment over PM2.5 pollution hazards through analysis of Chinese microblog			Type	Conference Article
	Year	2014	Publication	ISCRAM 2014 Conference Proceedings – 11th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2014
	Volume		Issue		Pages	722-726
	Keywords	Air pollution; Information systems; Time series analysis; Crisis; Pm2.5; Public opinions; Sentiment analysis; Social media analysis; Social aspects
	Abstract	Decision-making in crisis management can benefit from routine monitoring of the (social) media to discover the mass opinion on highly sensitive crisis events. We present an experiment that analyzes Chinese microblog data (extracted from Weibo.cn) to measure sentiment strength and its change in relation to the recent PM 2.5 air pollution events. The data were analyzed using SentiStrength algorithm together with a special sentiment words dictionary tailored and refined for Chinese language. The results of time series analysis on detected sentiment strength showed that less than one percent of the posts are strong-positive or strong negative. Weekly sentiment strength measures show symmetric changes in positive and negative strength, but overall trend moved towards more positive opinions. Special attention was given to sharp bursts of sentiment strength that coincide temporally with the occurrence of extreme social events. These findings suggest that sentiment strength analysis may generate useful alert and awareness of pending extreme social events.
	Address	Lanzhou University, Gansu, China; Penn State University, University Park, PA, United States
	Corporate Author				Thesis
	Publisher	The Pennsylvania State University	Place of Publication	University Park, PA	Editor	S.R. Hiltz, M.S. Pfaff, L. Plotnick, and P.C. Shih.
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3387	ISBN	9780692211946	Medium
	Track	Social Media in Crisis Response and Management	Expedition		Conference	11th International ISCRAM Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	939
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	Author	Yingjie Li; Seoyeon Park; Cornelia Caragea; Doina Caragea; Andrea Tapia
	Title	Sympathy Detection in Disaster Twitter Data			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	Word Embedding, Deep Learning, Machine Learning, Sympathy Tweets Detection
	Abstract	Nowadays, micro-blogging sites such as Twitter have become powerful tools for communicating with others in various situations. Especially in disaster events, these sites can be the best platforms for seeking or providing social support, of which informational support and emotional support are the most important types. Sympathy, a sub-type of emotional support, is an expression of one?s compassion or sorrow for a difficult situation that another person is facing. Providing sympathy to people affected by a disaster can help change people?s emotional states from negative to positive emotions, and hence, help them feel better. Moreover, detecting sympathy contents in Twitter can potentially be used for finding candidate donors since the emotion ?sympathy? is closely related to people who may be willing to donate. Thus, in this paper, as a starting point, we focus on detecting sympathy-related tweets. We address this task using Convolutional Neural Networks (CNNs) with refined word embeddings. Specifically, we propose a refined word embedding technique in terms of various pre-trained word vector models and show great performance of CNNs that use these refined embeddings in the sympathy tweet classification task. We also report experimental results showing that the CNNs with the refined word embeddings outperform not only traditional machine learning techniques, such as Naïve Bayes, Support Vector Machines and AdaBoost with conventional feature sets as bags of words, but also Long Short-Term Memory Networks.
	Address	University of Illinois at Chicago, United States of America;Kansas State University, United States of America;Pennsylvania State University, 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	1899
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	Author	Yang Zhang; William Drake; Yuhong Li; Christopher Zobel; Margaret Cowell
	Title	Fostering Community Resilience through Adaptive Learning in a Social Media Age: Municipal Twitter Use in New Jersey following Hurricane Sandy			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	Adaptive learning; disaster resilience; Hurricane Sandy; social media; Twitter
	Abstract	Adaptive learning capacity is a critical component of community resilience that describes the ability of a community to effectively gauge its vulnerability to the external environment and to make appropriate changes to its coping strategies. Traditionally, the relationship between government and community learning was framed within a deterministic paradigm. Learning outcomes were understood to result from the activities of central actors (i.e., government) and flow passively into the community. The emergence of social media is fundamentally changing the ways organizations and individuals collect and share information. Despite its growing acceptance, it remains to be determined how this shift in communication will ultimately affect community adaptive learning, and therefore, community resilience. This paper presents the initial results of a mixed-methods research effort that examined the use of Twitter in local municipalities from Monmouth County, NJ after Hurricane Sandy. Using a conceptual model of organizational learning, we examine the learning outcomes following the Hurricane Sandy experience.
	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	1236
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	Author	Yang Ishigaki; Yoshinori Matsumoto; Yutaka Matsuno; Kenji Tanaka
	Title	Participatory Radiation Information Monitoring with SNS after Fukushima			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	Facebook; Nuclear Disaster; Open Source Hardware; Social Inclusion
	Abstract	We developed a series of inexpensive but accurate mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the urgent desire of ordinary people to measure and share radiation levels in their milieus and to discuss the results of the Nuclear Disaster in Fukushima, Japan. This action research reports on a new style of pragmatic model of radiation monitoring, which employs the features of Participatory Design and Participatory Sensing and adopts modern communication platforms such as crowd-funding, open source development, and Facebook. This paper proposes an interaction model between the project management body, and other inclusive corroborators, e.g., ordinary users and experts, and focuses on three development phases of the project: start-up phase, evaluation phase, and operation phase. This paper also considers a reliability assurance model on disaster information sharing between the citizen layer and the official layer by data sharing and discussion activities in the POKEGA community.
	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	1243
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	Author	Yan Wang; Qi Wang; John Taylor
	Title	Loss of Resilience in Human Mobility across Severe Tropical Cyclones of Different Magnitudes			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	755-765
	Keywords	Disaster Resilience, Geo-social networking, Human mobility, Tropical Cyclones
	Abstract	Severe tropical cyclones impose threats on highly populated coastal urban areas, thereby, understanding and predicting human movements plays a critical role in evaluating disaster resilience of human society. However, limited research has focused on tropical cyclones and their influence on human mobility resilience. This preliminary study examined the strength and duration of human mobility perturbation across five significant tropical storms and their affected eight urban areas using Twitter data. The results suggest that tropical cyclones can significantly perturb human movements by changing travel frequencies and displacement probability distributions. While the power-law still best described the pattern of human movements, the changes in the radii of gyration were significant and resulted in perturbation and loss of resilience in human mobility. The findings deepen the understanding about human-environment interactions under extreme events, improve our ability to predict human movements using social media data, and help policymakers improve disaster evacuation and response.
	Address	University of Florida; Northeastern University; Georgia Institute of Technology
	Corporate Author				Thesis
	Publisher	Virginia Tech	Place of Publication	Blacksburg, VA (USA)	Editor	Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	978-1-949373-61-5	ISBN		Medium
	Track	Social Media for Disaster Response and Resilience	Expedition		Conference	18th International Conference on Information Systems for Crisis Response and Management
	Notes	yanw@ufl.edu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2370
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	Author	Yajie Li; Amanda Lee Hughes; Peter D. Howe
	Title	Communicating Crisis with Persuasion: Examining Official Twitter Messages on Heat Hazards			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	469-479
	Keywords	Persuasion, crisis communication, susceptibility, social media, heat hazards.
	Abstract	Official crisis messages need to be persuasive to promote appropriate public responses. However, little research has examined the content of crisis messages from a persuasion perspective, especially for natural hazards. This study deductively identifies five persuasive message factors (PMFs) applicable to natural hazards, including two under-examined health-related PMFs: health risk susceptibility and health impact. Using 2016 heat hazards as a case study, this paper content-analyzes heat-related Twitter messages (N=904) posted by eighteen U.S. National Weather Service Weather Forecast Offices according to the five PMFs. We find that the use of descriptions of hazard intensity is disproportionately high, with a lack of use of other PMFs. We also describe different types of statements used to signal the two health-related PMFs. We conclude with implications and recommendations relevant to practitioners and researchers in social media crisis communication.
	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	2124
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	Author	Xukun Li; Doina Caragea; Cornelia Caragea; Muhammad Imran; Ferda Ofli
	Title	Identifying Disaster Damage Images Using a Domain Adaptation Approach			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	image classification, disaster damage, domain adaptation, domain adversarial neural networks.
	Abstract	Approaches for effectively filtering useful situational awareness information posted by eyewitnesses of disasters, in real time, are greatly needed. While many studies have focused on filtering textual information, the research on filtering disaster images is more limited. In particular, there are no studies on the applicability of domain adaptation to filter images from an emergent target disaster, when no labeled data is available for the target disaster. To fill in this gap, we propose to apply a domain adaptation approach, called domain adversarial neural networks (DANN), to the task of identifying images that show damage. The DANN approach has VGG-19 as its backbone, and uses the adversarial training to find a transformation that makes the source and target data indistinguishable. Experimental results on several pairs of disasters suggest that the DANN model generally gives similar or better results as compared to the VGG-19 model fine-tuned on the source labeled data.
	Address	Department of Computer Science, Kansas State University, United States of America;Department of Computer Science, University of Illinois at Chicago, United States of America;Qatar Computing Research Institute, Hamad Bin Khalifa University, 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	1853
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	Author	Xukun Li; Doina Caragea
	Title	Improving Disaster-related Tweet Classification with a Multimodal 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	893-902
	Keywords	Multimodal Model; Tweet Classification; Deep Learning
	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.
	Address	Department of Computer Science, Kansas State University; Department of Computer Science, Kansas State University
	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	978-1-949373-27-79	ISBN	2411-3465	Medium
	Track	Social Media for Disaster Response and Resilie	Expedition		Conference	17th International Conference on Information Systems for Crisis Response and Management
	Notes	xukun@ksu.edu			Approved	no
	Call Number				Serial	2280
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	Author	Xiao Li; Julia Kotlarsky; Michael D. Myers
	Title	Crowdsourcing and the COVID-19 Response in China: An Actor-Network Perspective			Type	Conference Article
	Year	2023	Publication	Proceedings of the ISCRAM Asia Pacific Conference 2022	Abbreviated Journal	Proc. ISCRAM AP 2022
	Volume		Issue		Pages	240-246
	Keywords	Disaster; Crowdsourcing; Actor-Network; Social Media
	Abstract	Crowdsourcing, serving as a distributed problem-solving and production model, can help in the response to a disaster. The current literature focuses on the flow of crowdsourced information, but the question of how crowdsourcing contributes to physical disaster workflows remains to be addressed. Based on a case study of China’s response to COVID-19, this research aims to explore the role of crowdsourcing stakeholders and how they acted to respond to the outbreak. Actor network theory is applied as the lens to elucidate the roles of different heterogeneous actors. The preliminary results indicate that socio-technical actors activated, absorbed, associated, and aligned with each other to combat the pandemic. We suggest ways to augment the actor network to address potential future outbreaks.
	Address	University of Auckland; University of Auckland; University of Auckland
	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	2497
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	Author	William R. Smith; Keri K. Stephens; Brett Robertson; Jing Li; Dhiraj Murthy
	Title	Social Media in Citizen-Led Disaster Response: Rescuer Roles, Coordination Challenges, and Untapped Potential			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	639-648
	Keywords	Crisis communication, social media, emergent groups, mobile technology, emergency management
	Abstract	Widespread disasters can overload official agencies' capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semi-structured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens use social media to respond to calls for help from flooding victims.
	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	2138
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	Author	Wang, D.; Kogan, M.
	Title	Resonance+: Augmenting Collective Attention to Find Information on Public Cognition and Perception of Risk			Type	Conference Article
	Year	2023	Publication	Proceedings of the 20th International ISCRAM Conference	Abbreviated Journal	Iscram 2023
	Volume		Issue		Pages	487-500
	Keywords	Crisis Informatics; Social Media Data; Word Embedding; Cognitive Process; Protective Action Decision Model
	Abstract	Microblogging platforms have been increasingly used by the public and crisis managers in crisis. The increasing volume of data has made such platforms more difficult for officials to find on-the-ground information and understand the public’s perception of the evolving risks. The crisis informatics literature has proposed various technological solutions to find relevant information from social media. However, the cognitive processes of the affected population and their subsequent responses, such as perceptions, emotional and behavioral responses, are still under-examined at scale. Yet, such information is important for gauging public perception of risks, an important task for PIOs and emergency managers. In this work, we leverage the noise-cutting power of collective attention and take cues from the Protective Action Decision Model, to propose a method that estimates shifts in collective attention with a special focus on the cognitive processes of those affected and their subsequent responses.
	Address	University of Utah, School of Computing; University of Utah, School of Computing
	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	2411-3387	ISBN	979-8-218-21749-5	Medium
	Track	Social Media for Crisis Management	Expedition		Conference
	Notes	http://dx.doi.org/10.59297/IMVX7820			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2542
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	Author	Venkata Kishore Neppalli; Murilo Cerqueira Medeiros; Cornelia Caragea; Doina Caragea; Andrea Tapia; Shane Halse
	Title	Retweetability Analysis and Prediction during Hurricane Sandy			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Twitter; Retweetability Analysis; Retweetability Prediction; Hurricane Sandy; Disaster Events
	Abstract	Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.
	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	1389
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	Author	Venkata Kishore Neppalli; Cornelia Caragea; Doina Caragea
	Title	Deep Neural Networks versus Naive Bayes Classifiers for Identifying Informative Tweets during Disasters			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	677-686
	Keywords	deep neural networks, naive bayes classifiers, handcrafted features
	Abstract	In this paper, we focus on understanding the effectiveness of deep neural networks by comparison with the effectiveness of standard classifiers that use carefully engineered features. Specifically, we design various feature sets (based on tweet content, user details and polarity clues) and use these feature sets individually or in various combinations, with Naïve Bayes classifiers. Furthermore, we develop neural models based on Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with handcrafted architectures. We compare the two types of approaches in the context of identifying informative tweets posted during disasters, and show that the deep neural networks, in particular the CNN networks, are more effective for the task considered.
	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 CO -	Expedition		Conference
	Notes				Approved	no
	Call Number				Serial	2141
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	Author	Valerio Lorini; Javier Rando; Diego Saez-Trumper; Carlos Castillo
	Title	Uneven Coverage of Natural Disasters in Wikipedia: The Case of Floods			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	688-703
	Keywords	Social Media, News Values, Wikipedia, Natural Disasters, Floods.
	Abstract	The usage of non-authoritative data for disaster management provides timely information that might not be available through other means. Wikipedia, a collaboratively-produced encyclopedia, includes in-depth information about many natural disasters, and its editors are particularly good at adding information in real-time as a crisis unfolds. In this study, we focus on the most comprehensive version of Wikipedia, the English one. Wikipedia offers good coverage of disasters, particularly those having a large number of fatalities. However, by performing automatic content analysis at a global scale, we also show how the coverage of floods in Wikipedia is skewed towards rich, English-speaking countries, in particular the US and Canada. We also note how coverage of floods in countries with the lowest income is substantially lower than the coverage of floods in middle-income countries. These results have implications for analysts and systems using Wikipedia as an information source about disasters.
	Address	European Commission, Joint Research Centre (JRC), Ispra, Italy Universitat Pompeu Fabra, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; Wikimedia Foundation; Universitat Pompeu Fabra, Barcelona, Spain
	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	978-1-949373-27-63	ISBN	2411-3449	Medium
	Track	Social Media for Disaster Response and Resilie	Expedition		Conference	17th International Conference on Information Systems for Crisis Response and Management
	Notes	valerio.lorini@ec.europa.eu			Approved	no
	Call Number				Serial	2264
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	Author	Valerio Lorini; Carlos Castillo; Steve Peterson; Paola Rufolo; Hemant Purohit; Diego Pajarito; João Porto de Albuquerque; Cody Buntain
	Title	Social Media for Emergency Management: Opportunities and Challenges at the Intersection of Research and Practice			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	772-777
	Keywords	Crisis Informatics, Social Media, Workshop Report, Disaster Management
	Abstract	This paper summarizes key opportunities and challenges identified during the workshop “Social Media for Disaster Risk Management: Researchers Meet Practitioners” which took place online in November 2020. It constitutes a work-in-progress towards identifying new directions for research and development of systems that can better serve the information needs of emergency managers. Practitioners widely recognize the potential of accessing timely information from social media. Nevertheless, the discussion outlined some critical challenges for improving its adoption during crises. In particular, validating such information and integrating it with authoritative information and into more traditional information systems for emergency managers requires further work, and the negative impacts of misinformation and disinformation need to be prevented.
	Address	European Commission, Joint Research Centre (JRC), Ispra, Italy; Universitat Pompeu Fabra, Barcelona, Spain; Community Emergency Response Team, Montgomery County, Maryland, USA; European Commission, Joint Research Centre, Ispra, Italy; George Mason Univers
	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	valerio.lorini@ec.europa.eu			Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2372
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