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Author (up) 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 (up) Ferda Ofli; Firoj Alam; Muhammad Imran
Title Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response 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 802-811
Keywords Multimodal Deep Learning, Multimedia Content, Natural Disasters, Crisis Computing, Social Media.
Abstract Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
Address Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar
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 978-1-949373-27-71 ISBN 2411-3457 Medium
Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management
Notes fofli@hbku.edu.qa Approved no
Call Number Serial 2272
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Author (up) 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 (up) 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 (up) Muhammad Imran; Firoj Alam; Umair Qazi; Steve Peterson; Ferda Ofli
Title Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence 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 761-773
Keywords Social Media, Damage Assessment, Artificial Intelligence, Image Processing.
Abstract Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research.
Address Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Montgomery County, Maryland Community Emergency Response Team United States; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar
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 978-1-949373-27-68 ISBN 2411-3454 Medium
Track Social Media for Disaster Response and Resilie Expedition Conference 17th International Conference on Information Systems for Crisis Response and Management
Notes mimran@hbku.edu.qa Approved no
Call Number Serial 2269
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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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