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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 (up)
Notes Approved no
Call Number Serial 2141
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Author Kiran Zahra; Muhammad Imran; Frank O Ostermann
Title Understanding eyewitness reports on Twitter 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 687-695
Keywords social media, disaster response, eyewitness accounts
Abstract Social media platforms such as Twitter provide convenient ways to share and consume important information during disasters and emergencies. Information from bystanders and eyewitnesses can be useful for law enforcement agencies and humanitarian organizations to get firsthand and credible information about an ongoing situation to gain situational awareness among other uses. However, identification of eyewitness reports on Twitter is challenging for many reasons. This work investigates the sources of tweets and classifies them into three types (i) direct eyewitnesses, (ii) indirect eyewitness, and (iii) vulnerable accounts. Moreover, we investigate various characteristics associated with each kind of eyewitness account. We observe that words related to perceptual senses (feeling, seeing, hearing) tend to be present in direct eyewitness messages, whereas emotions, thoughts, and prayers are more common in indirect witnesses. We believe these characteristics can help make more efficient computational methods and systems in the future for automatic identification of eyewitness accounts.
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 (up)
Notes Approved no
Call Number Serial 2142
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Author Ryo Otaka; Osamu Uchida; Keisuke Utsu
Title Prototype of Notification and Status Monitoring System Using LINE Smartphone Application to Support Local Communities 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 450-458
Keywords Care, Application, Social media
Abstract Japanese society is aging rapidly, so an increasing number of households currently consists of only elderly single people or couples. We propose a system that uses LINE (a mobile communication application) for sending notices containing information from local governments to elderly or physically disabled people, as well as for efficient monitoring by local governments and social workers of the health conditions and statuses of such people. Our system can be used by anyone who has a smartphone with LINE installed. We have also conducted an operational test of a prototype of our system.
Address Tokai University; Tokai University; Tokai 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 (up)
Notes Approved no
Call Number Serial 1659
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Author Shuji Nishikawa; Osamu Uchida; Keisuke Utsu
Title Introduction of a Tracking Map to a Web Application for Location Recording and Rescue Request 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 459-468
Keywords Location information, Rescue request, Disaster
Abstract We developed a web application for location recording and rescue request using Twitter (T-Pl@ce). This application helps supported users (e.g., older adults, persons with disabilities, and children) who require support to share their location coordinates via Twitter. Supporting users (e.g., families, relatives, or neighbors) of the supported user can then check the location coordinates of the supported user when required. When the supported user needs to be rescued, he/she can post a rescue request on Twitter by pressing the “Rescue request” button on the application. In this study, we introduce the e-mail notification function to reliably notify a rescue request to the system administrator. In addition, to track the location of the supported user, we introduce a location tracking function. Then, the administrator, the emergency assistance employees (e.g., rescue experts or social workers), or the supporting user can refer to the request and the location tracking page and execute the support and rescue activities.
Address Tokai University; Tokai University; Tokai 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 (up)
Notes Approved no
Call Number Serial 1660
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Author Linda Plotnick; Starr Roxanne Hiltz; Sukeshini Grandhi; Julie Dugdale
Title Real or Fake? User Behavior and Attitudes Related to Determining the Veracity of Social Media Posts 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 439-449
Keywords Social media, trustworthiness, fake news
Abstract Citizens and Emergency Managers need to be able to distinguish “fake” (untrue) news posts from real news posts on social media during disasters. This paper is based on an online survey conducted in 2018 that produced 341 responses from invitations distributed via email and through Facebook. It explores to what extent and how citizens generally assess whether postings are “true” or “fake,” and describes indicators of the trustworthiness of content that users would like. The mean response on a semantic differential scale measuring how frequently users attempt to verify the news trustworthiness (a scale from 1-never to 5-always) was 3.37. The most frequent message characteristics citizens' use are grammar and the trustworthiness of the sender. Most respondents would find an indicator of trustworthiness helpful, with the most popular choice being a colored graphic. Limitations and implications for assessments of trustworthiness during disasters are discussed.
Address New Jersey Institute of Technology; Eastern Connecticut State University; New Jersey Institute of Technology; University of Grenoble
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 1665
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Author Jess Kropczynski; Rob Grace; Julien Coche; Shane Halse; Eric Obeysekare; Aurélie Montarnal; Frederick Bénaben; Andrea Tapia
Title Identifying Actionable Information on Social Media for Emergency Dispatch 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 428-438
Keywords Public Safety Answering Point (PSAP), Social Media, Qualitative Coding
Abstract Crisis informatics researchers have taken great interest in methods to identify information relevant to crisis events posted by digital bystanders on social media. This work codifies the information needs of emergency dispatchers and first responders as a method to identify actionable information on social media. Through a design workshop with public safety professionals at a Public-Safety Answering Point (PSAP) in the United States, we develop a set of information requirements that must be satisfied to dispatch first responders and meet their immediate situational awareness needs. We then present a manual coding scheme to identify information satisfying these requirements in social media posts and apply this scheme to fictitious tweets professionals propose as actionable information to better assess ways that this information may be communicated. Finally, we propose automated methods from previous literature in the field that can be used to implement these methods in the future.
Address University of Cincinnati; The Pennsylvania State University; coles des Mines d'Albi Carmaux; The Pennsylvania State University; The Pennsylvania State University; coles des Mines d'Albi Carmaux; The Pennsylvania State University; coles des Mines d'Albi Carmaux
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 (up)
Notes Approved no
Call Number Serial 1672
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Author Marta Poblet Balcell; Stan Karanasios; Vanessa Cooper
Title Look after Your Neighbours: Social Media and Vulnerable Groups during Extreme Weather Events 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 408-415
Keywords Social media, vulnerable populations, extreme weather events, emergency management organisations
Abstract Emergency management organisations across the world routinely use social media to reach out populations for preparedness and response to extreme weather events. In this paper we present a preliminary analysis of social media strategies towards vulnerable populations in the State of Victoria (Australia). Using the notion of vulnerability in an emergency management context (e.g. older persons, socially/geographically isolated persons, people with disabilities, refugee/recent migrant communities) we explore whether and how organisations address vulnerable groups with targeted messages. Our initial findings suggest that organisations do not tend to interact directly with these groups. Rather, reliance on 'information brokers' (intermediary organisations and individuals with an expected duty of care) seems to be a preferred strategy.
Address RMIT University; RMIT University; RMIT 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 (up)
Notes Approved no
Call Number Serial 1679
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Author Maryam Shahbazi; Christian Ehnis; Majid Shahbazi; Deborah Bunker
Title Tweeting from the Shadows: Social Media Convergence Behaviour During the 2017 Iran-Iraq Earthquake 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 416-427
Keywords Social Media Crisis Communication, Convergence Behaviour, Earthquake, Natural Disaster
Abstract Official policies, socioeconomic and demographic factors influence how individuals cope with, and respond to natural disasters. Understanding the impact of these factors in social media crisis communications studies is difficult. This paper focuses on convergence behaviour during social media crisis communication in an environment where the access to commercial social media platforms is highly restricted. This study is designed as a case which analyses 41,745 Tweets communicated during an earthquake event and for the two weeks after. This research aims to understand how different communities use social media services for communication during extreme events. The content of the Tweets shows users' attitudes toward government policies as well as the social difficulties of ethnic groups reflecting on the use of social media in crises communication. The results indicate a “political effect” on this online crisis communication. This behaviour was not expected and has been underreported in the current body of knowledge.
Address The University of Sydney; The University of Sydney; Azad University; The University of Sydney
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 (up)
Notes Approved no
Call Number Serial 1682
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Author Hongmin Li; Xukun Li; Doina Caragea; Cornelia Caragea
Title Comparison of Word Embeddings and Sentence Encodings for Generalized Representations in Crisis Tweet Classifications Type Conference Article
Year 2018 Publication Proceedings of ISCRAM Asia Pacific 2018: Innovating for Resilience – 1st International Conference on Information Systems for Crisis Response and Management Asia Pacific. Abbreviated Journal Iscram Ap 2018
Volume Issue Pages 480-493
Keywords Word Embeddings, Sentence Encodings, Reduced Tweet Representation, Crisis Tweet Classification
Abstract Many machine learning and natural language processing techniques, including supervised and domain adaptation algorithms, have been proposed and studied in the context of filtering crisis tweets. However, applying these approaches in real-time is still challenging because of time-critical requirements of emergency response operations and also diversities and unique characteristics of emergency events. In this paper, we explore the idea of building “generalized” classifiers for filtering crisis tweets that can be pre-trained, and are thus ready to use in real-time, while generalizing well on future disasters/crises data. We propose to achieve this using simple feature based adaptation with tweet representations based on word embeddings and also sentence-level embeddings, representations which do not rely on unlabeled data to achieve domain adaptations and can be easily implemented. Given that there are different types of word/sentence embeddings that are widely used, we propose to compare them to get a general idea about which type works better with crisis tweets classification tasks. Our experimental results show that GloVe embeddings in general work better with the datasets used in our evaluation, and that the supervised algorithms used in our experiments benefit from GloVe embeddings trained specifically on crisis data. Furthermore, our experimental results show that following GloVe, the sentence embeddings have great potential in crisis tweet tasks.
Address Kansas State University; Kansas State University; Kansas State University; Kansas State University
Corporate Author Thesis
Publisher Massey Univeristy Place of Publication Albany, Auckland, New Zealand Editor Kristin Stock; Deborah Bunker
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Track Social Media and Community Engagement Supporting Resilience Building Expedition Conference (up)
Notes Approved no
Call Number Serial 1689
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Author Dario Salza; Edoardo Arnaudo; Giacomo Blanco; Claudio Rossi
Title A 'Glocal' Approach for Real-time Emergency Event Detection in Twitter 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 570-583
Keywords Emergency; Event Detection; Social Media; Twitter; Incremental Clustering
Abstract Social media like Twitter offer not only an unprecedented amount of user-generated content covering developing emergencies but also act as a collector of news produced by heterogeneous sources, including big and small media companies as well as public authorities. However, this volume, velocity, and variety of data constitute the main value and, at the same time, the key challenge to implement and automatic detection and tracking of independent emergency events from the real-time stream of tweets. Leveraging online clustering and considering both textual and geographical features, we propose, implement, and evaluate an algorithm to automatically detect emergency events applying a ‘glocal’ approach, i.e., offering a global coverage while detecting events at local (municipality level) scale.
Address LINKS Foundation; LINKS Foundation; LINKS Foundation; LINKS Foundation
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2440
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Author Cody Buntain; Richard Mccreadie; Ian Soboroff
Title Incident Streams 2021 Off the Deep End: Deeper Annotations and Evaluations in Twitter 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 584-604
Keywords Emergency Management; Crisis Informatics; Twitter; Categorization; Priorization; Multi-Modal; Public Safety; PSCR; TREC
Abstract This paper summarizes the final year of the four-year Text REtrieval Conference Incident Streams track (TREC-IS), which has produced a large dataset comprising 136,263 annotated tweets, spanning 98 crisis events. Goals of this final year were twofold: 1) to add new categories for assessing messages, with a focus on characterizing the audience, author, and images associated with these messages, and 2) to enlarge the TREC-IS dataset with new events, with an emphasis of deeper pools for sampling. Beyond these two goals, TREC-IS has nearly doubled the number of annotated messages per event for the 26 crises introduced in 2021 and has released a new parallel dataset of 312,546 images associated with crisis content – with 7,297 tweets having annotations about their embedded images. Our analyses of this new crisis data yields new insights about the context of a tweet; e.g., messages intended for a local audience and those that contain images of weather forecasts and infographics have higher than average assessments of priority but are relatively rare. Tweets containing images, however, have higher perceived priorities than tweets without images. Moving to deeper pools, while tending to lower classification performance, also does not generally impact performance rankings or alter distributions of information-types. We end this paper with a discussion of these datasets, analyses, their implications, and how they contribute both new data and insights to the broader crisis informatics community.
Address University of Maryland, College Park (UMD); University of Glasgow; National Institute of Standards and Technology (NIST)
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2441
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Author Pooneh Mousavi; Cody Buntain
Title “Please Donate for the Affected”: Supporting Emergency Managers in Finding Volunteers and Donations in Twitter Across Disasters 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 605-622
Keywords social media; crisis in formatics; volunteers; donations; emergency support functions
Abstract Despite the outpouring of social support posted to social media channels in the aftermath of disaster, finding and managing content that can translate into community relief, donations, volunteering, or other recovery support is difficult due to the lack of sufficient annotated data around volunteerism. This paper outlines three experiments to alleviate these difficulties. First, we estimate to what degree volunteerism content from one crisis is transferable to another by evaluating the consistency of language in volunteer-and donation-related social media content across 78 disasters. Second it introduces methods for providing computational support in this emergency support function and developing semi-automated models for classifying volunteer-and donation-related social media content in new disaster events. Results show volunteer-and donation-related social media content is sufficiently similar across disasters and disaster types to warrant transferring models across disasters, and we evaluate simple resampling techniques for tuning these models. We then introduce and evaluate a weak-supervision approach to integrate domain knowledge from emergency response officers with machine learningmodelstoimproveclassification accuracy andacceleratethisemergencysupportinnewevents. This method helps to overcome the scarcity in data that we observe related to volunteer-and donation-related social media content.
Address University of Maryland, College Park; University of Maryland, College Park
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2442
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Author Thomas Papadimos; Nick Pantelidis; Stelios Andreadis; Aristeidis Bozas; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris
Title Real-time Alert Framework for Fire Incidents Using Multimodal Event Detection on Social Media Streams 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 623-635
Keywords Alert framework; social media; event detection; kernel density estimation; community detection
Abstract The frequency of wildfires is growing day by day due to vastly climate changes. Forest fires can have a severe impact on human lives and the environment, which can be minimised if the population has early and accurate warning mechanisms. To date, social media are able to contribute to early warning with the additional, crowd-sourced information they can provide to the emergency response workers during a crisis event. Nevertheless, the detection of real-world fire incidents using social media data, while filtering out the unavoidable noise, remains a challenging task. In this paper, we present an alert framework for the real-time detection of fire events and we propose a novel multimodal event detection model, which fuses both probabilistic and graph methodologies and is evaluated on the largest fires in Spain during 2019.
Address Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologies Institute Thessaloniki, Greece;Centre for Research & Technology Hellas Information Technologie
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2443
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Author Kiran Zahra; Rahul Deb Das; Frank O. Ostermann; Ross S. Purves
Title Towards an Automated Information Extraction Model from Twitter Threads during Disasters 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 637-653
Keywords Social media threads; Text summarization; Disasters; Lexicons; Information extraction models; Word embeddings
Abstract Social media plays a vital role as a communication source during large-scale disasters. The unstructured and informal nature of such short individual posts makes it difficult to extract useful information, often due to a lack of additional context. The potential of social media threads– sequences of posts– has not been explored as a source of adding context and more information to the initiating post. In this research, we explored Twitter threads as an information source and developed an information extraction model capable of extracting relevant information from threads posted during disasters. We used a crowdsourcing platform to determine whether a thread adds more information to the initial tweet and defined disaster-related information present in these threads into six themes– event reporting, location, time, intensity, casualty and damage reports, and help calls. For these themes, we created the respective thematic lexicons from WordNet. Moreover, we developed and compared four information extraction models trained on GloVe, word2vec, bag-of-words, and thematic bag-of-words to extract and summarize the most critical information from the threads. Our results reveal that 70 percent of all threads add information to the initiating post for various disaster-related themes. Furthermore, the thematic bag-of-words information extraction model outperforms the other algorithms and models for preserving the highest number of disaster-related themes.
Address University of Zurich; University of Zurich, IBM; University of Twente; University of Zurich
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2444
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Author Gaëtan Caillaut; Cécile Gracianne; Nathalie Abadie; Guillaume Touya; Samuel Auclair
Title Automated Construction of a French Entity Linking Dataset to Geolocate Social Network Posts in the Context of Natural Disasters 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 654-663
Keywords Automated geotagging; French Entity Linking; Wikipedia; Twitter; Crisis Management; Natural Disaster
Abstract During natural disasters, automatic information extraction from Twitter posts is a valuable way to get a better overview of the field situation. This information has to be geolocated to support effective actions, but for the vast majority of tweets, spatial information has to be extracted from texts content. Despite the remarkable advances of the Natural Language Processing field, this task is still challenging for current state-of-the-art models because they are not necessarily trained on Twitter data and because high quality annotated data are still lacking for low resources languages. This research in progress address this gap describing an analytic pipeline able to automatically extract geolocatable entities from texts and to annotate them by aligning them with the entities present in Wikipedia/Wikidata resources. We present a new dataset for Entity Linking on French texts as preliminary results, and discuss research perspectives for enhancements over current state-of-the-art modeling for this task.
Address BRGM; BRGM; LASTIG, Univ Gustave Eiffel, IGN-ENSG; LASTIG, Univ Gustave Eiffel, IGN-ENSG; BRGM
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2445
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Author Jens Kersten; Jan Bongard; Friederike Klan
Title Gaussian Processes for One-class and Binary Classification of Crisis-related Tweets Type Conference Article
Year 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 664-673
Keywords Gaussian Process; One-class Classification; Twitter; Overload Reduction; Crisis Informatics
Abstract Overload reduction is essential to exploit Twitter text data for crisis management. Often used pre-trained machine learning models require training data for both, crisis-related and off-topic content. However, this task can also be formulated as a one-class classification problem in which labeled off-topic samples are not required. Gaussian processes (GPs) have great potential in both, binary and one-class settings and are therefore investigated in this work. Deep kernel learning combines the representative power of text embeddings with the Bayesian formalism of GPs. Motivated by this, we investigate the potential of deep kernel models for the task of classifying crisis-related tweet texts with special emphasis on cross-event applications. Compared to standard binary neural networks, first experiments with one-class GP models reveal a great potential for realistic scenarios, offering a fast and flexible approach for interactive model training without requiring off-topic training samples and comprehensive expert knowledge (only two model parameters involved).
Address German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany; German Aerospace Center– Jena, Germany
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track Social Media for Crisis Management Expedition Conference (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2446
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Author Carlo Alberto Bono; Barbara Pernici; Jose Luis Fernandez-Marquez; Amudha Ravi Shankar; Mehmet Oguz Mülâyim; Edoardo Nemni
Title TriggerCit: Early Flood Alerting using Twitter and Geolocation – A Comparison with Alternative Sources 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 674-686
Keywords Social Media; Disaster management; Early Alerting
Abstract Rapid impact assessment in the immediate aftermath of a natural disaster is essential to provide adequate information to international organisations, local authorities, and first responders. Social media can support emergency response with evidence-based content posted by citizens and organisations during ongoing events. In the paper, we propose TriggerCit: an early flood alerting tool with a multilanguage approach focused on timeliness and geolocation. The paper focuses on assessing the reliability of the approach as a triggering system, comparing it with alternative sources for alerts, and evaluating the quality and amount of complementary information gathered. Geolocated visual evidence extracted from Twitter by TriggerCit was analysed in two case studies on floods in Thailand and Nepal in 2021. The system respectively returned a large scale and a local scale alert, both in a timely manner and accompanied by a valid geographical description, while providing information complementary to existing disaster alert mechanisms.
Address Politecnico di Milano- DEIB;Politecnico di Milano- DEIB;University of Geneva;University of Geneva;Artificial Intelligence Research Institute (IIIA-CSIC); United Nations Satellite Centre (UNOSAT), United Nations Institute for Training and Research (UNITAR)
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2447
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Author Ahmed Alnuhayt; Suvodeep Mazumdar; Vitaveska Lanfranchi; Frank Hopfgartner
Title Understanding Reactions to Misinformation – A Covid-19 Perspective 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 687-700
Keywords Misinformation; social reactions; twitter; people; COVID-19
Abstract The increasing use of social media as an information source brings further challenges – social media platforms can be an excellent medium for disseminating public awareness and critical information, that can be shared across large populations. However, misinformation in social media can have immense implications on public health, risking the effectiveness of health interventions as well as lives. This has been particularly true in the case of COVID-19 pandemic, with a range of misinformation, conspiracy theories and propaganda being spread across social channels. In our study, through a questionnaire survey, we set out to understand how members of the public interact with different sources when looking for information on COVID-19. We explored how participants react when they encounter information they believe to be misinformation. Through a set of three behaviour tasks, synthetic misinformation posts were provided to the participants who chose how they would react to them. In this work in progress study, we present initial findings and insights into our analysis of the data collected. We highlight what are the most common reactions to misinformation and also how these reactions are different based on the type of misinformation.
Address Information School University of Sheffield; Information School University of Sheffield; Computer Science University of Sheffield; Information School University of Sheffield
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2448
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Author Nils Bourgon; Benamara Farah; Alda Mari; Véronique Moriceau; Gaetan Chevalier; Laurent Leygue; Yasmine Djadda
Title Are Sudden Crises Making me Collapse? Measuring Transfer Learning Performances on Urgency Detection 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 701-709
Keywords Sudden crises; Transfer learning; Few-shot learning; Zero-shot learning; Social media content
Abstract This paper aims at measuring transfer learning performances across different types of crises related to sudden or unexpected events (like earthquakes, terror attacks, explosions, technological incidents) that cannot be foreseen by emergency services and on the occurrence of which they have virtually no control. Although sudden crises are present in most existing crisis datasets, as far as we are aware, no one studied their impact on classifiers performances when evaluated in an out-of-type scenario in which models are tested on a particular type of crisis unseen during training. Our contribution is threefold: (1) A new dataset of about 3,800 French tweets related to four sudden events that occurred in France annotated for both relatedness (i.e., useful vs. not useful for emergency responders) and urgency (i.e., not useful vs. urgent vs. not urgent), (2) A set of monotask and multitask zero-shot learning experiments to transfer knowledge across events and types, and finally, (3) Experiments involving few-shot learning to measure the amount of sudden events instances needed during training to guarantee good performances. When compared to a cross-event setting, our preliminary results are encouraging and show that transfer from predictable ecological crisis to sudden events is feasible and constitutes a first step towards real-time crisis management systems from social media content.
Address IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; IJN, CNRS/ENS/EHESS PSL University; IRIT, Université de Toulouse, CNRS, Toulouse INP, UT3; DGSCGC SDAIRS; DGSCGC SDAIRS
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2449
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Author Hafiz Budi Firmansyah; Jesus Cerquides; Jose Luis Fernandez-Marquez
Title Ensemble Learning for the Classification of Social Media Data in Disaster Response Type Conference Article
Year 2022 Publication ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2022
Volume Issue Pages 710-718
Keywords Ensemble learning; image classification; social media; disaster response
Abstract Social media generates large amounts of almost real-time data which has proven valuable in disaster response. Specially for providing information within the first 48 hours after a disaster occurs. However, this potential is poorly exploited in operational environments due to the challenges of curating social media data. This work builds on top of the latest research on automatic classification of social media content, proposing the use of ensemble learning to help in the classification of social media images for disaster response. Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Experimental results show that ensemble learning is a valuable technology for the analysis of social media images for disaster response,and could potentially ease the integration of social media data within an operational environment.
Address Citizen Cyberlab, CUI, University of Geneva, Switzerland; Citizen Cyberlab, CUI, University of Geneva, Switzerland; IIIA-CSIC, Barcelona, Spain
Corporate Author Thesis
Publisher Place of Publication Tarbes, France Editor Rob Grace; Hossein Baharmand
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-82-8427-099-9 Medium
Track Social Media for Crisis Management Expedition Conference (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2450
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Author Shivam Sharma; Cody Buntain
Title Bang for your Buck: Performance Impact Across Choices in Learning Architectures for Crisis Informatics 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 719-736
Keywords Incident Streams; TREC; TRECIS; crisis informatics
Abstract Over the years, with the increase in social media engagement, there has been an in increase in various pipelines to analyze, classify and prioritize crisis-related data on various social media platforms. These pipelines utilize various data augmentation methods to counter imbalanced crisis data, sophisticated and off-the-shelf models for training. However, there is a lack of comprehensive study which compares these methods for the various sections of a pipeline. In this study, we split a general crisis-related pipeline into 3 major sections, namely, data augmentation, model selection, and training methodology. We compare various methods for each of these sections and then present a comprehensive evaluation of which section to prioritize based on the results from various pipelines. We compare our results against two separate tasks, information classification and priority scoring for crisis-related tweets. Our results suggest that data augmentation, in general,improves the performance. However, sophisticated, state-of-the-art language models like DeBERTa only show performance gain in information classification tasks, and models like RoBERTa tend to show a consistent performance increase over our presented baseline consisting of BERT. We also show that, though training two separate task-specific BERT models does show better performance than one BERT model with multi-task learning methodology over an imbalanced dataset, multi-task learning does improve performance for more sophisticated model like DeBERTa with a much more balanced dataset after augmentation.
Address New Jersey Institute of Technology; New Jersey Institute of Technology
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2451
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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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2472
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Author Ly Dinh; Sumeet Kulkarni; Pingjing Yang; Jana Diesner
Title Reliability of Methods for Extracting Collaboration Networks from Crisis-related Situational Reports and Tweets Type Conference Article
Year 2023 Publication Proceedings of the ISCRAM Asia Pacific Conference 2022 Abbreviated Journal Proc. ISCRAM AP 2022
Volume Issue Pages 181-195
Keywords Collaboration Networks; Natural Language Processing; Interorganizational Collaboration; Situational Awareness
Abstract Assessing the effectiveness of crisis response is key to improving preparedness and adapting policies. One method for response evaluation is reviewing actual response activities and interactions. Response reports are often available in the form of natural language text data. Analyzing a large number of such reports requires automated or semi automated solutions. To improve the trustworthiness of methods for this purpose, we empirically validate the reliability of three relation extraction methods that we used to construct interorganizational collaboration networks by comparing them against human-annotated ground truth (crisis-specific situational reports and tweets). For entity extraction, we find that using a combination of two off-the-shelf methods (FlairNLP and SpaCy) is optimal for situational reports data and one method (SpaCy) for tweets data. For relation extraction, we find that a heuristics-based model that we built by leveraging word co-occurrence and deep and shallow syntax as features and training it on domain-specific text data outperforms two state-of-the-art relation extraction models (Stanford OpenIE and OneIE) that were pre-trained on general domain data. We also find that situational reports, on average, contain less entities and relations than tweets, but the extracted networks are more closely related to collaboration activities mentioned in the ground truth. As it is widely known that general domain tools might need adjustment to perform accurately in specific domains, we did not expect the tested off-the-shelf tools to perform highly accurately. Our point is to rather identify what accuracy one could reasonably expect when leveraging available resources as-is for domain specific work (in this case, crisis informatics), what errors (in terms of false positives and false negatives) to expect, and how to account for that.
Address University of South Florida; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign; University of Illinois at Urbana-Champaign
Corporate Author Thesis
Publisher Massey Unversity Place of Publication Palmerston North, New Zealand Editor Thomas J. Huggins, V.L.
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-0-473-66845-7 Medium
Track Social Media for Disaster Response Expedition Conference (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2492
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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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2497
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Author Robert Power; Bella Robinson; Mark Cameron
Title Insights from a Decade of Twitter Monitoring for Emergency Management Type Conference Article
Year 2023 Publication Proceedings of the ISCRAM Asia Pacific Conference 2022 Abbreviated Journal Proc. ISCRAM AP 2022
Volume Issue Pages 247-257
Keywords Crisis Coordination; Disaster Management; Situation Awareness; Social Media; System Architecture; Twitter
Abstract The Emergency Situation Awareness (ESA) tool began as a research study into automated web text mining to support emergency management use cases. It started in late 2009 by investigating how people respond on Twitter to specific emergency events and we quickly realized that every emergency situation is different and preemptively defining keywords to search for content on Twitter beforehand would likely miss important information. So, in late September 2011 we established location-based searches with the aim of collecting all the tweets published in Australia and New Zealand. This was the beginning of over a decade of collecting and processing tweets to help emergency response agencies and crisis coordination centres use social media content as a new channel of information to support their work practices and to engage with the community impacted by emergency events. This journey has seen numerous challenges overcome to continuously maintain a tweet stream for an operational system. This experience allows us to derive insights into the changing use of Twitter over this time. In this paper we present some of the lessons we’ve learned from maintaining a Twitter monitoring system for emergency management use cases and we provide some insights into the changing nature of Twitter usage by users over this period.
Address CSIRO Data61; CSIRO Data61; CSIRO Data61
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 (up)
Notes Approved no
Call Number ISCRAM @ idladmin @ Serial 2498
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