| Records |
| Author |
Shivam Sharma; Cody Buntain |
| Title |
An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
808-815 |
| Keywords |
covid19, twitter, trecis, cross-validation, machine learning, transfer learning |
| Abstract |
In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. |
| Address |
New Jersey Institute of Technology; New Jersey Institute of Technology |
| Corporate Author |
|
Thesis  |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Anouck Adrot; Rob Grace; Kathleen Moore; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-61-5 |
ISBN |
|
Medium |
|
| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
cbuntain@njit.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2375 |
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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 |
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| 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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| |
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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 |
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Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| 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 |
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Thesis |
|
| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
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| 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 |
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Thesis |
|
| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Thesis |
|
| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
|
Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Thesis |
|
| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Thesis |
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| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
|
| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Thesis |
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| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Thesis |
|
| Publisher |
|
Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| 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 |
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Thesis |
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| 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 |
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 |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2492 |
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| |
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| |
| Author |
Xiao Li; Julia Kotlarsky; Michael D. Myers |
| Title |
Crowdsourcing and the COVID-19 Response in China: An Actor-Network Perspective |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
240-246 |
| Keywords |
Disaster; Crowdsourcing; Actor-Network; Social Media |
| Abstract |
Crowdsourcing, serving as a distributed problem-solving and production model, can help in the response to a disaster. The current literature focuses on the flow of crowdsourced information, but the question of how crowdsourcing contributes to physical disaster workflows remains to be addressed. Based on a case study of China’s response to COVID-19, this research aims to explore the role of crowdsourcing stakeholders and how they acted to respond to the outbreak. Actor network theory is applied as the lens to elucidate the roles of different heterogeneous actors. The preliminary results indicate that socio-technical actors activated, absorbed, associated, and aligned with each other to combat the pandemic. We suggest ways to augment the actor network to address potential future outbreaks. |
| Address |
University of Auckland; University of Auckland; University of Auckland |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
978-0-473-66845-7 |
Medium |
|
| Track |
Social Media for Disaster Response |
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2497 |
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| |
|
| |
| Author |
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 |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2498 |
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| |
|
| |
| Author |
Dilini Rajapaksha; Kacper Sokol; Jeffrey Chan; Flora Salim; Mukesh Prasad; Mahendra Samarawickrama |
| Title |
Analysing Donors’ Behaviour in Non-profit Organisations for Disaster Resilience |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
258-267 |
| Keywords |
Disaster Response; Social Media; Donors’ Behaviour; Australian Bushfires |
| Abstract |
With the advancement and proliferation of technology, non-profit organisations have embraced social media platforms to improve their operational capabilities through brand advocacy, among many other strategies. The effect of such social media campaigns on these institutions, however, remains largely underexplored, especially during disaster periods. This work introduces and applies a quantitative investigative framework to understand how social media influence the behaviour of donors and their usage of these platforms throughout (natural) disasters. More specifically, we explore how on-line engagement – as captured by Facebook interactions and Google search trends – corresponds to the donors’ behaviour during the catastrophic 2019–2020 Australian bushfire season. To discover this relationship, we analyse the record of donations made to the Australian Red Cross throughout this period. Our exploratory study reveals that social media campaigns are effective in encouraging on-line donations made via a dedicated website. We also compare this mode of giving to more regular, direct deposit gifting. |
| Address |
RMIT University; RMIT University; RMIT University; UNSW Sydney; University of Technology Sydney; Australian Red Cross |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
978-0-473-66845-7 |
Medium |
|
| Track |
Social Media for Disaster Response |
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2499 |
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|
| |
| Author |
Guillermo Romera Rodriguez |
| Title |
Parler, Capitol Riots, Alt-Right and Radicalization in Social Media |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the ISCRAM Asia Pacific Conference 2022 |
Abbreviated Journal |
Proc. ISCRAM AP 2022 |
| Volume |
|
Issue |
|
Pages |
268-277 |
| Keywords |
Social Media; Parler; Sentiment Analysis; Alt-Right |
| Abstract |
Social media platforms have risen in popularity since their inception. These platforms have since then come to be at the forefront of controversies, from being accused of election interference to, more recently, disseminating fake news and campaigns to sway political behavior. One such episode took place on January 6 when a group of individuals stormed the United States Capitol, and the social media platform Parler came under scrutiny. The platform was accused of being a place for right-wing extremists and Trump supporters who claimed the 2020 election was fraudulent. Initial reports suggested these individuals used Parler to organize and call others to action. This paper explores the feasibility of using social media to detect alt-right radicalization and examines its possible relation to the Capitol Insurrection and Parler. Moreover, we examine if those events could have been detected and averted through the investigation of the platform. |
| Address |
Pennsylvania State University; Pennsylvania State University; Pennsylvania State University |
| Corporate Author |
|
Thesis |
|
| Publisher |
Massey Unversity |
Place of Publication |
Palmerston North, New Zealand |
Editor |
Thomas J. Huggins, V.L. |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
978-0-473-66845-7 |
Medium |
|
| Track |
Social Media for Disaster Response |
Expedition |
|
Conference |
|
| Notes |
|
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2500 |
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| |
| Author |
Yu, X.; Chen, J.; Liu, J. |
| Title |
Examining the influence of social media on individual’s protective action taking during Covid-19 in China |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
295-308 |
| Keywords |
Public Crisis; Social Mediated Crisis Communication Model; Risk Perception; Protective Action |
| Abstract |
In the context of COVID-19, this study utilizes the Social Mediated Crisis Communication Model (SMCC) and the Protective Action Decision Model (PADM) to investigate the relationship between social media users' protective actions and crisis information during public health crises in China. By constructing a structural equation model, this study aims to identify the influencing factors that affect social media users' personal’s cognitive, emotional, and behavioral reactions given crisis relevant information. Results findings are that warning information can significantly increase risk perception; emotional responses are not significantly affected by warning information and risk perception; risk perception has a negative impact on information gathering and sharing behavior; risk perception has a significant mediating effect on the relationship between information features and protective action. |
| Address |
University of International Business and Economics |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
2411-3387 |
ISBN |
979-8-218-21749-5 |
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/HPVH6600 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2527 |
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| |
| Author |
Long, Z.; McCreadiem, R.; Imran, M. |
| Title |
CrisisViT: A Robust Vision Transformer for Crisis Image Classification |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
309-319 |
| Keywords |
Social Media Classification; Crisis Management; Deep Learning; Vision Transformers; Supervised Learning |
| Abstract |
In times of emergency, crisis response agencies need to quickly and accurately assess the situation on the ground in order to deploy relevant services and resources. However, authorities often have to make decisions based on limited information, as data on affected regions can be scarce until local response services can provide first-hand reports. Fortunately, the widespread availability of smartphones with high-quality cameras has made citizen journalism through social media a valuable source of information for crisis responders. However, analyzing the large volume of images posted by citizens requires more time and effort than is typically available. To address this issue, this paper proposes the use of state-of-the-art deep neural models for automatic image classification/tagging, specifically by adapting transformer-based architectures for crisis image classification (CrisisViT). We leverage the new Incidents1M crisis image dataset to develop a range of new transformer-based image classification models. Through experimentation over the standard Crisis image benchmark dataset, we demonstrate that the CrisisViT models significantly outperform previous approaches in emergency type, image relevance, humanitarian category, and damage severity classification. Additionally, we show that the new Incidents1M dataset can further augment the CrisisViT models resulting in an additional 1.25% absolute accuracy gain. |
| Address |
University of Glasgow |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/SDSM9194 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2528 |
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| |
| Author |
McCreadie, R.; Buntain, C. |
| Title |
CrisisFACTS: Buidling and Evaluating Crisis Timelines |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
320-339 |
| Keywords |
Emergency Management; Crisis Informatics News; Twitter; Facebook; Reddit; Wikipedia; Summarization |
| Abstract |
Between 2018 and 2021, the Incident Streams track (TREC-IS) developed standard approaches for classifying information types and criticality of tweets during crises. While successful in producing substantial collections of labeled data, TREC-IS as a data challenge had several limitations: It only evaluated information at type-level rather than what was reported; it only used Twitter data; and it lacked measures of redundancy in system output. This paper introduces Crisis Facts and Cross-Stream Temporal Summarization (CrisisFACTS), a new data challenge piloted in 2022 and developed to address these limitations. The CrisisFACTS framework recasts TREC-IS into an event-summarization task using multiple disaster-relevant data streams and a new fact-based evaluation scheme, allowing the community to assess state-of-the-art methods for summarizing disaster events Results from CrisisFACTS in 2022 include a new test-collection comprising human-generated disaster summaries along with multi-platform datasets of social media, crisis reports and news coverage for major crisis events. |
| Address |
University of Glasgow; University of Maryland, College Park (UMD) |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/JVQZ9405 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2529 |
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| |
| Author |
Encarnación, T.; Wilks, C.R. |
| Title |
Role of Expressed Emotions on the Retransmission of Help-Seeking Messages during Disasters |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
340-352 |
| Keywords |
Social Amplification; Retweet Prediction; Crisis Informatics |
| Abstract |
Emergency managers rely on formal and informal communication channels to identify needs in post-disaster environments. Message retransmission is a critical factor to ensure that help-seekers are identified by disaster responders. This paper uses a novel annotated dataset of Twitter posts from four major disasters that impacted the United States in 2021, to quantify the effect that expressed emotions and support typology have on retransmission. Poisson regression models are estimated, and the results show that messages seeking instrumental support are more likely to be retransmitted. Expressions of anger, fear, and sadness increase overall retweets. Moreover, expressions of anger, anticipation, or sadness increase the likelihood of retransmission for messages that seek instrumental help. |
| Address |
College of Business Administration University of Missouri-St |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/DDXJ4655 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2530 |
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| |
| Author |
Lamsal, R.; Read, M.R.; Karunasekera, S. |
| Title |
A Twitter narrative of the COVID-19 pandemic in Australia |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
353-370 |
| Keywords |
Crisis Informatics; Situational Awareness; Topic Modeling; Granger Causality; Network Analysis |
| Abstract |
Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management. |
| Address |
The University of Melbourne |
| Corporate Author |
|
Thesis |
|
| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
Hosssein Baharmand |
Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
1 |
| ISSN |
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ISBN |
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Medium |
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| Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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| Notes |
http://dx.doi.org/10.59297/GQED8281 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2531 |
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| Author |
Pereira, J.; Fidalgo, R.; Lotufo, R.; Nogueira, R. |
| Title |
Crisis Event Social Media Summarization with GPT-3 and Neural Reranking |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
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Issue |
|
Pages |
371-384 |
| Keywords |
Crisis Management; Social Media; Multi-Document Summarization; Query-Based Summarization. |
| Abstract |
Managing emergency events, such as natural disasters, requires management teams to have an up-to-date view of what is happening throughout the event. In this paper, we demonstrate how a method using a state-of-the-art open-sourced search engine and a large language model can generate accurate and comprehensive summaries by retrieving information from social media and online news sources. We evaluated our method on the TREC CrisisFACTS challenge dataset using automatic summarization metrics (e.g., Rouge-2 and BERTScore) and the manual evaluation performed by the challenge organizers. Our approach is the best in comprehensiveness despite presenting a high redundancy ratio in the generated summaries. In addition, since all pipeline components are few-shot, there is no need to collect training data, allowing us to deploy the system rapidly. Code is available at https://github.com/neuralmind-ai/visconde-crisis-summarization. |
| Address |
Centro de Inform´atica, Universidade Federal de Pernambuco; NeuralMind |
| Corporate Author |
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Thesis |
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| Publisher |
University of Nebraska at Omaha |
Place of Publication |
Omaha, USA |
Editor |
Jaziar Radianti; Ioannis Dokas; Nicolas Lalone; Deepak Khazanchi |
| Language |
English |
Summary Language |
|
Original Title |
|
| Series Editor |
Hosssein Baharmand |
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/JJYT4136 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2532 |
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