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Author (up) 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 (up) 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 (up) 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 (up) Jess Kropczynski; Rob Grace; Shane Halse; Doina Caragea; Cornelia Caragea; Andrea Tapia
Title Refining a Coding Scheme to Identify Actionable Information on Social Media 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 Dispatch, Public Safety Answering Point (PSAP), Social Media, Qualitative Coding.
Abstract This paper describes the use of a previously established qualitative coding scheme developed through a design workshop with public safety professionals, and applied the schema to social media data collecting during crises. The intention of applying this scheme to existing crisis datasets was to acquire training data for machine learning. Applying the coding scheme to social media data revealed that additional subcategories of the coding scheme are necessary to satisfy information requirements necessary to dispatch first responders to an incident. The coding scheme was refined and adapted into a set of instructions for qualitative coders on Amazon Mechanical Turk. The contribution of this work is a coding scheme that is more directly related to the information needs of public safety professionals. Implications of early results using the refined coding scheme are discussed in terms of proposed automated methods to identify actionable information for dispatch of first responders during emergency incidents.
Address Uiniversity of Cincinnati, United States of America;The Pennsylvania State University;Kansas State University;University of Illinois at Chicago
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 1981
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Author (up) Nasik Muhammad Nafi; Avishek Bose; Sarthak Khanal; Doina Caragea; William H. Hsu
Title Abstractive Text Summarization of Disaster-Related Documents 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 881-892
Keywords Disaster Reporting; Text Summarization; Information Extraction; Reinforcement Learning; Evaluation Metrics
Abstract Abstractive summarization is intended to capture key information from the full text of documents. In the application domain of disaster and crisis event reporting, key information includes disaster effects, cause, and severity. While some researches regarding information extraction in the disaster domain have focused on keyphrase extraction from short disaster-related texts like tweets, there is hardly any work that attempts abstractive summarization of long disaster-related documents. Following the recent success of Reinforcement Learning (RL) in other domains, we leverage an RL-based state-of-the-art approach in abstractive summarization to summarize disaster-related documents. RL enables an agent to find an optimal policy by maximizing some reward. We design a novel hybrid reward metric for the disaster domain by combining \underline{Vec}tor Similarity and \underline{Lex}icon Matching (\textit{VecLex}) to maximize the relevance of the abstract to the source document while focusing on disaster-related keywords. We evaluate the model on a disaster-related subset of a CNN/Daily Mail dataset consisting of 104,913 documents. The results show that our approach produces more informative summaries and achieves higher \textit{VecLex} scores compared to the baseline.
Address Kansas State University; Kansas State University; Kansas State University; Kansas State University; 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 978-1-949373-27-78 ISBN 2411-3464 Medium
Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management
Notes nnafi@ksu.edu Approved no
Call Number Serial 2279
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Author (up) Reza Mazloom; HongMin Li; Doina Caragea; Muhammad Imran; Cornelia Caragea
Title Classification of Twitter Disaster Data Using a Hybrid Feature-Instance Adaptation Approach 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 727-735
Keywords Tweet classification, Domain adaptation, Matrix factorization, k-Nearest Neighbors, Disaster response
Abstract Huge amounts of data that are generated on social media during emergency situations are regarded as troves of critical information. The use of supervised machine learning techniques in the early stages of a disaster is challenged by the lack of labeled data for that particular disaster. Furthermore, supervised models trained on labeled data from a prior disaster may not produce accurate results, given the inherent variation between the current and the prior disasters. To address the challenges posed by the lack of labeled data for a target disaster, we propose to use a hybrid feature-instance adaptation approach based on matrix factorization and the k nearest neighbors algorithm, respectively. The proposed hybrid adaptation approach is used to select a subset of the source disaster data that is representative for the target disaster. The selected subset is subsequently used to learn accurate Naive Bayes classifiers for the target disaster.
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 2146
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Author (up) 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 (up) 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 (up) 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 (up) 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 (up) 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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