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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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	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
	Notes				Approved	no
	Call Number	ISCRAM @ idladmin @			Serial	2472
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	Author	Jennings Anderson; Marina Kogan; Melissa Bica; Leysia Palen; Kenneth Anderson; Rebecca Morss; Julie Demuth; Heather Lazrus; Olga Wilhelmi
	Title	Far Far Away in Far Rockaway: Responses to Risks and Impacts during Hurricane Sandy through First-Person Social Media Narratives			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	Crisis Informatics; Hurricane Sandy; Protective Decision Making; Risk Perception; Social Media; Twitter
	Abstract	When Hurricane Sandy swept over the US eastern seaboard in October 2012, it was the most tweeted about event at the time. However, some of the most affected areas were underrepresented in the social media conversation about Sandy. Here, we examine the hurricane-related experiences and behaviors shared on Twitter by residents of Far Rockaway, a New York City neighborhood that is geographically and socioeconomically vulnerable to disasters, which was significantly affected by the storm. By carefully filtering the vast Twitter data, we focus on 41 Far Rockaway residents who offer rich personal accounts of their experience with Sandy. Analyzing their first-person narratives, we see risk perception and protective decision-making behavior in their data. We also find themes of invisibility and neglect when residents expressed feeling abandoned by the media, the city government, and the overall relief efforts in the aftermath of 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	ISCRAM @ idladmin @			Serial	1388
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	Author	Venkata Kishore Neppalli; Murilo Cerqueira Medeiros; Cornelia Caragea; Doina Caragea; Andrea Tapia; Shane Halse
	Title	Retweetability Analysis and Prediction during Hurricane Sandy			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Twitter; Retweetability Analysis; Retweetability Prediction; Hurricane Sandy; Disaster Events
	Abstract	Twitter is a very important source for obtaining information, especially during events such as natural disasters. Users can spread information in Twitter either by crafting new posts, which are called ?tweets,? or by using retweet mechanism to re-post the previously created tweets. During natural disasters, identifying how likely a tweet is to be highly retweeted is very important since it can help promote the spread of good information in a network such as Twitter, as well as it can help stop the spread of misinformation, when corroborated with approaches that identify trustworthy information or misinformation, respectively. In this paper, we present an analysis on retweeted tweets to determine several aspects affecting retweetability. We then extract features from tweets? content and user account information and perform experiments to develop models that automatically predict the retweetability of a tweet in the context of the Hurricane Sandy.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1389
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	Author	Apoorva Chauhan; Amanda Lee Hughes
	Title	Online Mentioning Behavior during Hurricane Sandy: References, Recommendations, and Rebroadcasts			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	Crisis Informatics; Social Media; Emergency Management
	Abstract	Large-scale crisis events require coordination between the many responding stakeholders to provide timely, relevant, and accurate information to the affected public. In this paper, we examine how social media can support these coordinated public information efforts. This research considers how emergency responders mentioned different organizations, institutions, and individuals by examining the social media communications of police and fire departments during Hurricane Sandy. We find that these departments use mentions to reference other sources of information, recommend credible information and sources, and rebroadcast information. These mentions offer insight into how emergency responders fit within a broader crisis information network and the types of entities that responders trust and recommend to provide information to the public.
	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	1390
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	Author	Elodie Fichet; John Robinson; Dharma Dailey; Kate Starbird
	Title	Eyes on the Ground: Emerging Practices in Periscope Use during Crisis Events			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Social Media; Periscope; Twitter; Crisis Informatics; Emergency Management
	Abstract	This empirical analysis examines the use of the live-streaming application Periscope in three crises that occurred in 2015. Qualitative analyses of tweets relating to the Amtrak derailment in Philadelphia, Baltimore protests after Freddie Grey?s death, and Hurricane Joaquin flooding in South Carolina reveal that this recently deployed application is being used by both citizens and journalists for information sharing, crisis coverage and commentary. The accessibility and immediacy of live video directly from crisis situations, and the embedded chats which overlay on top of a video feed, extend the possibilities of real-time interaction between remote crowds and those on the ground in a crisis. These empirical findings suggest several potential challenges and opportunities for responders.
	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	ISCRAM @ idladmin @			Serial	1391
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	Author	Yuhong Li; Christopher Zobel
	Title	Small Businesses and Social Media Usage in the 2013 Colorado Floods			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Social Media; Small Business; Recovery; Disaster
	Abstract	The recovery of small businesses from a disaster is critical to community recovery. Such businesses can be extremely vulnerable to disasters, particularly because they often occupy a single location and have a localized customer base. Although social media is an effective platform for information dissemination, and has been extensively used in a disaster context, the way in which small businesses use social media in this context, and the effectiveness of those efforts, are still not well understood. With this in mind, this paper uses the 2013 floods along the Front Range in Colorado as a case study to help improve our understanding of how small businesses use social media in disaster situations. Characterizing the organizations' behavior involves using both qualitative and quantitative approaches, and the paper focuses on an initial qualitative analysis.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1392
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	Author	Pragna Debnath; Saniul Haque; Somprakash Bandyopadhyay; Siuli Roy
	Title	Post-disaster Situational Analysis from WhatsApp Group Chats of Emergency Response Providers			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
	Abstract	Use of social media has established itself as one of the important information carriers in the field of disaster management. However, use of Twitter and Facebook by victims, first responders and others generates information that is varied, unstructured and unreliable. On the other hand, NGOs, operating in the disaster area, are often involved in intra-organizational communication using messaging apps like WhatsApp, and their group interactions can help in gathering meaningful data for situational analysis and need assessment. Our focus is to automate the process of filtering relevant information, query-based clustering of pertinent information from a WhatsApp group conversation of a specific volunteer group, so that situation analysis and need assessment can be done more rapidly. We have evaluated our scheme using WhatsApp chat log of a medical volunteer group in two post-disaster scenarios and concluded that it can provide valuable insights about region-specific resource requirements and allocation for effective decision making.
	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	1393
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	Author	Antonin Segault; Federico Tajariol; Yang Ishigaki; Ioan Roxin
	Title	#geiger 2: Developing Guidelines for Radiation Measurements Sharing on Social Media			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; Nuclear Post-Accident; Radiation; Robots; Syntax
	Abstract	Radiation measurements are key information in post-nuclear accident situations. Automated Twitter accounts have been used to share the readings, but often in an incomplete way from the perspective of data sharing and risk communication between citizen and radiation experts. In this paper, we investigate the requirements for radiation measurements completeness, by analyzing the perceived usefulness of several metadata items that may go along the measurement itself. We carried out a benchmark of existing uses, and conducted a survey with both experts and lay citizens. We thus produced a set of guidelines regarding the metadata that should be used, and the way to publish it.
	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	1394
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	Author	Shannon Daly; James A. Thom
	Title	Mining and Classifying Image Posts on Social Media to Analyse Fires			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	Flickr; Image Analytics; Geotags; Geocoding
	Abstract	We propose a methodology to study the occurrence of fires through image posts on Flickr; crowd-sourcing information from a noisy social media dataset can estimate the presence of fires. We collect several years worth of photos and associated metadata using fire-related search terms. We use an image classification model to detect geotagged photos that are further analysed to determine if a fire event did occur at a particular time and place. Furthermore, a case study investigates image features and spatio-temporal elements in the metadata, as well as location information contained in camera EXIF data.
	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	ISCRAM @ idladmin @			Serial	1395
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	Author	Muhammad Imran; Prasenjit Mitra; Jaideep Srivastava
	Title	Cross-Language Domain Adaptation for Classifying Crisis-Related Short Messages			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Social Media; Tweets Classification; Domain Adaptation
	Abstract	Rapid crisis response requires real-time analysis of messages. After a disaster happens, volunteers attempt to classify tweets to determine needs, e.g., supplies, infrastructure damage, etc. Given labeled data, supervised machine learning can help classify these messages. Scarcity of labeled data causes poor performance in machine training. Can we reuse old tweets to train classifiers? How can we choose labeled tweets for training? Specifically, we study the usefulness of labeled data of past events. Do labeled tweets in different language help? We observe the performance of our classifiers trained using different combinations of training sets obtained from past disasters. We perform extensive experimentation on real crisis datasets and show that the past labels are useful when both source and target events are of the same type (e.g. both earthquakes). For similar languages (e.g., Italian and Spanish), cross-language domain adaptation was useful, however, when for different languages (e.g., Italian and English), the performance decreased.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1396
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	Author	Cornelia Caragea; Adrian Silvescu; Andrea Tapia
	Title	Identifying Informative Messages in Disasters using Convolutional Neural Networks			Type	Conference Article
	Year	2016	Publication	ISCRAM 2016 Conference Proceedings ? 13th International Conference on Information Systems for Crisis Response and Management	Abbreviated Journal	ISCRAM 2016
	Volume		Issue		Pages
	Keywords	Informative Tweets Classification; Disaster Events; Convolutional Neural Networks
	Abstract	Social media is a vital source of information during any major event, especially natural disasters. Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate to help themselves. However, with the exponential increase in the volume of social media data, so comes the increase in data that are irrelevant to a disaster, thus, diminishing peoples? ability to find the information that they need in order to organize relief efforts, find help, and potentially save lives. In this paper, we present an approach to identifying informative messages in social media streams during disaster events. Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the ?bag of words? and n-grams as features on several datasets of messages from flooding events.
	Address
	Corporate Author				Thesis
	Publisher	Federal University of Rio de Janeiro	Place of Publication	Rio de Janeiro, Brasil	Editor	A. Tapia; P. Antunes; V.A. Bañuls; K. Moore; J. Porto
	Language	English	Summary Language	English	Original Title
	Series Editor		Series Title		Abbreviated Series Title
	Series Volume		Series Issue		Edition
	ISSN	2411-3388	ISBN	978-84-608-7984-9	Medium
	Track	Social Media Studies	Expedition		Conference	13th International Conference on Information Systems for Crisis Response and Management
	Notes				Approved	no
	Call Number				Serial	1397
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	Author	Emma Potter
	Title	Balancing conflicting operational and communications priorities: social media use in an emergency management organization			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	Emergency Management; Social Media; Internal Communication; Disasters; Ethnography
	Abstract	Social media are now widely used by affected members of the public during an emergency. As these platforms have become mainstream, governments have responded to the public?s expectation that information is available online, particularly during disasters. Emergency management organizations (EMOs) now widely use social media to communicate with the public alongside occasional intelligence gathering. While EMOs increasingly use social media, breakdowns in internal communication can inhibit the dissemination of timely information to their online followers. Drawing on a two-year ethnography at the Queensland Fire and Emergency Services (QFES), an Australian EMO, this paper outlines how the organization uses social media to disseminate information during emergencies and identifies the internal tensions around its use. These tensions include the prioritization of operational duties over public information responsibilities, and the difficulties around requesting and receiving information from operational personnel located on the ground.
	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	1398
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	Author	Louis Ngamassi; Thiagarajan Ramakrishnan; Shahedur Rahman
	Title	Examining the Role of Social Media in Disaster Management from an Attribution Theory Perspective			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	Attribution Theory; Social Media; Disaster Management; Disaster Management Phases
	Abstract	This paper is related to the use of social media for disaster management by humanitarian organizations. The past decade has seen a significant increase in the use of social media to manage humanitarian disasters. It seems, however, that it has still not been used to its full potential. In this paper, we examine the use of social media in disaster management through the lens of Attribution Theory. Attribution Theory posits that people look for the causes of events, especially unexpected and negative events. The two major characteristics of disasters are that they are unexpected and have negative outcomes/impacts. Thus, Attribution Theory may be a good fit for explaining social media adoption patterns by emergency managers. We propose a model, based on Attribution Theory, which is designed to understand the use of social media during the mitigation and preparedness phases of disaster management. We also discuss the theoretical contributions and some practical implications. This study is still in its nascent stage and is research in progress.
	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	1399
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