| Records |
| Author |
Yang Zhang; William Drake; Yuhong Li; Christopher Zobel; Margaret Cowell |
| Title |
Fostering Community Resilience through Adaptive Learning in a Social Media Age: Municipal Twitter Use in New Jersey following Hurricane Sandy |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
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| Keywords |
Adaptive learning; disaster resilience; Hurricane Sandy; social media; Twitter |
| Abstract |
Adaptive learning capacity is a critical component of community resilience that describes the ability of a community to effectively gauge its vulnerability to the external environment and to make appropriate changes to its coping strategies. Traditionally, the relationship between government and community learning was framed within a deterministic paradigm. Learning outcomes were understood to result from the activities of central actors (i.e., government) and flow passively into the community. The emergence of social media is fundamentally changing the ways organizations and individuals collect and share information. Despite its growing acceptance, it remains to be determined how this shift in communication will ultimately affect community adaptive learning, and therefore, community resilience. This paper presents the initial results of a mixed-methods research effort that examined the use of Twitter in local municipalities from Monmouth County, NJ after Hurricane Sandy. Using a conceptual model of organizational learning, we examine the learning outcomes following the Hurricane Sandy experience. |
| Address |
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| Corporate Author |
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Thesis |
|
Publisher  |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1236 |
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| |
| Author |
Robert Power; Bella Robinson; John Colton; Mark Cameron |
| Title |
A Case Study for Monitoring Fires with Twitter |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
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| Keywords |
Disaster Management; Situational Awareness; social media; Twitter |
| Abstract |
This paper presents a user configurable monitoring system to track in near-real-time tweets describing fire events. The system targets fire related words in a user defined region of interest published on Twitter which are further processed by a text classifier to determine if they describe a known fire event of interest. The system was motivated from a case study that examined a corpus of tweets posted during active bushfires. This demonstrated that useful information is available on Twitter about fire events from people who are in the vicinity.
We present an overview of the system describing how it is initially configured by a user to focus on specific fire events in Australia, the development of a text classifier to identify tweets of interest, especially those with accompanying photos, and the monitoring system that can track multiple events at once. |
| Address |
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| Corporate Author |
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Thesis |
|
| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1237 |
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| |
| Author |
Ntalla Athanasia; Ponis T. Stavros |
| Title |
Twitter as an instrument for crisis response: The Typhoon Haiyan case study |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
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| Keywords |
Crisis Management; emergency response; Haiyan; social media; Twitter |
| Abstract |
The research presented in this paper attempts an initial evaluation of Twitter as an instrument for emergency response in the context of a recent crisis event. The case of the 2013 disaster, when typhoon Haiyan hit Philippines is examined by analyzing nine consecutive days of Twitter messages and comparing them to the actual events. The results indicate that during disasters, Twitter users tend to post messages to enhance situation awareness and to motivate people to act. Furthermore, tweets were found reliable and provided valuable information content, supporting the argument that Twitter presents a very good potential to become a useful tool in situations where rapid emergency response is essential. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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| Series Editor |
|
Series Title |
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Abbreviated Series Title |
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| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1238 |
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| Author |
Antonin Segault; Federico Tajariol; Ioan Roxin |
| Title |
#geiger : Radiation Monitoring Twitter Bots for Nuclear Post-Accident Situations |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
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| Keywords |
bots; long-term period; nuclear post-accident; radiations; Twitter |
| Abstract |
In the last decade, people have increasingly relied on social media platforms such as Twitter to share information on the response to a natural or a man-made disaster. This paper focuses on the aftermath of the Fukushima Daiichi nuclear disaster. Since the disaster, victims and volunteers have been sharing relevant information about radiation measurements by means of social media. The aim of this research is to explore the diffusion of information produced and shared by Twitter bots, to understand the degree of popularity of these sources and to check if these bots deliver original radiation measurements. |
| Address |
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| Corporate Author |
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Thesis |
|
| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1239 |
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| Author |
Robin Peters; João Porto de Albuquerque |
| Title |
Investigating images as indicators for relevant social media messages in disaster management |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
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| Keywords |
Disaster Management; Flood; Germany; social media; Volunteered Geographic Information |
| Abstract |
The use of social media during disasters has received increasing attention in studies of the past few years. Existing research is mostly focused upon analyzing text-based messages from social media platforms such as Twitter, while image-based platforms have not been extensively addressed hitherto. However, pictures taken on-the-ground can offer reliable and valuable information for improving situation awareness and could be used as proxy indicators for relevance. To test this hypothesis, this work explores various social media platforms, including image- and text-based ones in the case of floods in Saxony 2013, Germany. Results show that there is a significant association between disaster-related messages containing images and their proximity to the disaster event. Hence, the existence of an image within a social media message can serve as an indicator for high probability of relevant content, and thus can be used for enhancing information extraction from social media towards improving situation awareness. |
| Address |
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| Corporate Author |
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Thesis |
|
| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1240 |
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| |
| Author |
Imen Bizid; Patrice Boursier; Jacques Morcos; Sami Faiz |
| Title |
A Classification Model for the Identification of Prominent Microblogs Users during a Disaster |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
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| Keywords |
Disaster Management; Information retrieval; Microblogs |
| Abstract |
Content shared in microblogs during disasters is expressed in various formats and languages. This diversity makes the information retrieval process more complex and computationally infeasible in real time. To address this, we propose a classification model for the identification of prominent users who are sharing relevant and exclusive information during the disaster. Users who have shared at least one tweet about the disaster are modeled using three kinds of time-sensitive features, including topical, social and geographical features. Then, these users are classified into two classes using a linear Support Vector Machine (SVM) to evaluate them over the extracted features and identify the most prominent ones. The first results using the actual dataset, show that our model has a high accuracy by detecting most of the prominent users. Moreover, we demonstrate that all the proposed features used by our model are indispensable to achieve this high accuracy. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
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| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1241 |
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| |
| Author |
Gerhard Backfried; Christian Schmidt; Gerald Quirchmayr |
| Title |
Cross-Media Linking in Times of Disaster |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Crisis communication; data collection; natural disasters; Situational Awareness; social media |
| Abstract |
Many possible links and connections can be observed between the different types of media used for communication during a crisis. These links can be detected and assembled to provide a more complete picture of events. They can be categorized according to the type of destination which yields important information for the gathering process as well as concerning general patterns of how platforms are connected. Tweets, posts and comments thus become parts of larger, linked sets of documents forming compound-documents. These documents stretch across media borders and platforms and provide context and broader information for individual entries. In the current paper we describe some of the links and linking behavior encountered during the floods in Central Europe of 2013 from the perspective of Twitter and Facebook. |
| Address |
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| Corporate Author |
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Thesis |
|
| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1242 |
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| |
| Author |
Yang Ishigaki; Yoshinori Matsumoto; Yutaka Matsuno; Kenji Tanaka |
| Title |
Participatory Radiation Information Monitoring with SNS after Fukushima |
Type |
Conference Article |
| Year |
2015 |
Publication |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
ISCRAM 2015 |
| Volume |
|
Issue |
|
Pages |
|
| Keywords |
Facebook; Nuclear Disaster; Open Source Hardware; Social Inclusion |
| Abstract |
We developed a series of inexpensive but accurate mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the urgent desire of ordinary people to measure and share radiation levels in their milieus and to discuss the results of the Nuclear Disaster in Fukushima, Japan. This action research reports on a new style of pragmatic model of radiation monitoring, which employs the features of Participatory Design and Participatory Sensing and adopts modern communication platforms such as crowd-funding, open source development, and Facebook. This paper proposes an interaction model between the project management body, and other inclusive corroborators, e.g., ordinary users and experts, and focuses on three development phases of the project: start-up phase, evaluation phase, and operation phase. This paper also considers a reliability assurance model on disaster information sharing between the citizen layer and the official layer by data sharing and discussion activities in the POKEGA community. |
| Address |
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| Corporate Author |
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Thesis |
|
| Publisher |
University of Agder (UiA) |
Place of Publication |
Kristiansand, Norway |
Editor |
L. Palen; M. Buscher; T. Comes; A. Hughes |
| Language |
English |
Summary Language |
English |
Original Title |
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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 |
|
Edition |
|
| ISSN |
2411-3387 |
ISBN |
9788271177881 |
Medium |
|
| Track |
Social Media Studies |
Expedition |
|
Conference |
ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management |
| Notes |
|
Approved |
yes |
| Call Number |
|
Serial |
1243 |
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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 |
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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 |
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| 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 |
|
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/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 |
|
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 |
|
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/JJYT4136 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2532 |
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| Author |
Zou, H.P.; Caragea, C.; Zhou, Y.; Caragea, D. |
| Title |
Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
385-395 |
| Keywords |
Crisis Tweet Classification; Semi-Supervised Few-Shot Learning; Pseudo-Labeling; TextMixUp. |
| Abstract |
The shared real-time information about natural disasters on social media platforms like Twitter and Facebook plays a critical role in informing volunteers, emergency managers, and response organizations. However, supervised learning models for monitoring disaster events require large amounts of annotated data, making them unrealistic for real-time use in disaster events. To address this challenge, we present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting where only a small number of annotated data is required. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data, mimicking the early stage of a disaster. Through integrating effective semi-supervised learning ideas and incorporating TextMixUp, CrisisMatch achieves performance improvement on two disaster datasets of 11.2% on average. Further analyses are also provided for the influence of the number of labeled data and out-of-domain results. |
| Address |
University of Illinois Chicago; Kansas State University |
| 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/FWXE4933 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2533 |
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| Author |
Fatehkia, M.; Imran, M.; Weber, I. |
| Title |
Towards Real-time Remote Social Sensing via Targeted Advertising |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
396-406 |
| Keywords |
Remote Social Sensing; Real-Time Polling; Flood Mapping; Facebook Advertising |
| Abstract |
Social media serves as an important communication channel for people affected by crises, creating a data source for emergency responders wanting to improve situational awareness. In particular, social listening on Twitter has been widely used for real-time analysis of crisis-related messages. This approach, however, is often hindered by the small fraction of (hyper-)localized content and by the inability to explicitly ask affected populations about aspects with the most operational value. Here, we explore a new form of social media data collected through targeted poll ads on Facebook. Using geo-targeted ads during flood events in six countries, we show that it is possible to collect thousands of poll responses within hours of launching the ad campaign, and at a cost of a few (US dollar) cents per response. We believe that this flexible, fast, and affordable data collection can serve as a valuable complement to existing approaches. |
| Address |
Qatar Computing Research Institute; Qatar Computing Research Institute; Saarland Informatics Campus |
| 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/NEFN8739 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2534 |
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| Author |
Rode-Hasinger, S.; Haberle, M.; Racek, D.; Kruspe, A.; Zhu Xiao Xiang |
| Title |
TweEvent: A dataset of Twitter messages about events in the Ukraine conflict |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
407-416 |
| Keywords |
Conflict; Ukraine; Dataset; Social Media; NLP |
| Abstract |
Information about incidents within a conflict, e.g., shelling of an area of interest, is scattered amongst different data or media sources. For example, the ACLED dataset continuously documents local incidents recorded within the context of a specific conflict such as Russia’s war in Ukraine. However, these blocks of information might be incomplete. Therefore, it is useful to collect data from several sources to enrich the information pool of a certain incident. In this paper, we present a dataset of social media messages covering the same war events as those collected in the ACLED dataset. The information is extracted from automatically geocoded Twitter text data using state-of-the-art natural language processing methods based on large pre-trained language models (LMs). Our method can be applied to various textual data sources. Both the data as well as the approach can serve to help human analysts obtain a broader understanding of conflict events. |
| Address |
Technical University of Munich; Technical University of Munich; Ludwig-Maximilians-Universitat M¨unchen; Technische Hochschule N¨urnberg; Technical University of Munich |
| Corporate Author |
|
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 |
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| Series Volume |
|
Series Issue |
|
Edition |
1 |
| ISSN |
|
ISBN |
|
Medium |
|
| Track |
Social Media for Crisis Management |
Expedition |
|
Conference |
|
| Notes |
http://dx.doi.org/10.59297/AIDF1102 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2535 |
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| Author |
Chauhan, A. |
| Title |
Humor-Based COVID-19 Twitter Accounts |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
417-427 |
| Keywords |
COVID-19; Twitter; Humor; Crisis Named Resources |
| Abstract |
Crisis Named Resources (or CNRs) are social media pages and accounts named after a crisis event. Using the COVID-19 Pandemic as a case study, we identified and examined the role of CNRs that shared humor on Twitter. Our analyses showed that humor-based CNRs shared virus-related rumors, stigma, safety measures, opinions, sarcasm, and news updates. These resources also shared the overall anger and frustration over the year 2020. We conclude by discussing the critical role of humor based CNRs in crisis response. |
| Address |
Concordia University of Edmonton |
| 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/YHDI4576 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2536 |
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| Author |
Tasneem, F.; Chakraborty, S.; Chy, A.N. |
| Title |
An Early Synthesis of Deep Neural Networks to Identify Multimodal Informative Disaster Tweets |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
428-438 |
| Keywords |
Early Fusion; Crisis Tweets; BERT-LSTM; ResNet50; Multimodal Framework |
| Abstract |
Twitter is always worthwhile in facilitating communication during disasters. It helps in raising situational awareness and undertaking disaster control actions as quickly as possible to alleviate the miseries. But the noisy essence of Twitter causes difficulty in distinguishing relevant information from the heterogeneous contents. Therefore, extracting informative tweets is a substantial task to help in crisis intervention. Analyzing only the text or image content of the tweet often misses necessary insights which might be helpful during disasters. In this paper, we propose a multimodal framework to address the challenges of identifying informative crisis-related tweets containing both texts and images. Our presented approach incorporates an early fusion strategy of BERT-LSTM and ResNet50 networks which effectively learns from the joint representation of texts and images. The experiments and evaluation on the benchmark CrisisMMD dataset show that our fusion method surpasses the baseline by 7% and substantiates its potency over the unimodal systems. |
| Address |
University of Chittagong; University of Chittagong; University of Chittagong |
| 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/OMIR7766 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2537 |
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| Author |
Herrera, L.C.; Gjøsæter, T. |
| Title |
Leveraging Crisis Informatics Experts: A co-creating approach for validation of social media research insights |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
439-448 |
| Keywords |
Card Sorting Workshop; Practice-Based Research; Crisis Informatics; Support Information System; Validation. |
| Abstract |
Validation of findings is a challenge in practice-based research. While analysis is being conducted and findings are being constructed out of data collected in a defined period, practitioners continue with their activities. This issue is exacerbated in the field of crisis management, where high volatility and personnel turnover make the capacity to attend research demands scarce. Therefore, conducting classic member validation is logistically challenging for the researcher. The need for rigor and validity calls for alternative mechanisms to fulfill requirements for academic research. This article presents an approach for validation of results of a qualitative study with public organizations that use social media as a source of information in the context of crisis management. The unavailability of original interview-objects to validate our findings resulted in an alternative validation method that leveraged experts in crisis informatics. By presenting our approach, we contribute to encouraging rigor in qualitative research while maintaining the relationship between practice and academia. |
| Address |
University of Agder; University of Agder |
| 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/MHCV5804 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2538 |
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| Author |
St. Denis, L.A.; Hughes, A.L. |
| Title |
Use of Statistics in Disaster by Local Individuals: An Examination of Tweets during COVID-19 |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
449-458 |
| Keywords |
Social Media; Statistics; COVID-19; Pandemic |
| Abstract |
We report on how individuals local to the US state of Colorado used statistics in tweets to make sense of the early stages of the COVID-19 pandemic. Tweets provided insight into how people interpreted statistical data, sometimes incorrectly, which has implications for crisis responders tasked with understanding public perceptions and providing accurate information. With widespread concerns about the accuracy and quality of online information, we show how monitoring public reactions to and uses of statistics on social media is important for improving crisis communication. Findings suggest that statistics can be a powerful tool for making sense of a crisis and coping with the stress and uncertainty of a global, rapidly evolving event like the COVID-19 pandemic. We conclude with broader implications for how crisis responders might improve their communications around statistics to the public, and suggestions for how this research might be expanded to look at other types of disasters. |
| Address |
CIRES, Earth Lab University of Colorado; Crisis Informatics Lab Brigham Young University |
| 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/KBIJ7756 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2539 |
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| Author |
Nurollahian, S.; Talegaonkar, I.; Bell, A.Z.; Kogan, M. |
| Title |
Factors Affecting Public’s Engagement with Tweets by Authoritative Sources During Crisis |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
459-477 |
| Keywords |
Crisis Informatics; Social Media; Public Engagement; Authoritative Sources; Topic Modeling |
| Abstract |
People increasingly use social media at the time of crisis, which produces a social media data deluge, where the public may find it difficult to locate trustworthy and credible information. Therefore, they often turn to authoritative sources: official individuals and organizations who are trusted to provide reliable information. It is then imperative that their credible messages reach and engage the widest possible audience, especially among those affected. In this study, we explore the role of metadata and linguistic factors in facilitating three types of engagement — retweets, replies, and favorites— with posts by authoritative sources. We find that many factors are similarly important across models (popularity, sociability, activity). However, some features are salient for only a specific type of engagement. We conclude by providing guidance to authoritative sources on how they may optimize specific types of engagement: retweets for information propagation, replies for in-depth sense-making, and favorites for cross-purpose visibility. |
| Address |
University of Utah; University of Utah; University of Utah; University of Utah |
| 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/AVHJ5856 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2540 |
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| Author |
Cruz, J.A. dela; Hendrickx, I.; Larson, M. |
| Title |
Towards XAI for Information Extraction on Online Media Data for Disaster Risk Management |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
478-486 |
| Keywords |
Disaster Risk Management; Information Extraction; Explainable AI (XAI); Explainabilit |
| Abstract |
Disaster risk management practitioners have the responsibility to make decisions at every phase of the disaster risk management cycle: mitigation, preparedness, response and recovery. The decisions they make affect human life. In this paper, we consider the current state of the use of AI in information extraction (IE) for disaster risk management (DRM), which makes it possible to leverage disaster information in social media. We consolidate the challenges and concerns of using AI for DRM into three main areas: limitations of DRM data, limitations of AI modeling and DRM domain-specific concerns, i.e., bias, privacy and security, transparency and accountability, and hype and inflated expectations. Then, we present a systematic discussion of how explainable AI (XAI) can address the challenges and concerns of using AI for IE in DRM. |
| Address |
Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies, Centre for Language and Speech Technology, Radboud University; Centre for Language Studies,Inst. for Computing and Information Sciences,Radboud University |
| 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/BHAE3912 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2541 |
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| Author |
Wang, D.; Kogan, M. |
| Title |
Resonance+: Augmenting Collective Attention to Find Information on Public Cognition and Perception of Risk |
Type |
Conference Article |
| Year |
2023 |
Publication |
Proceedings of the 20th International ISCRAM Conference |
Abbreviated Journal |
Iscram 2023 |
| Volume |
|
Issue |
|
Pages |
487-500 |
| Keywords |
Crisis Informatics; Social Media Data; Word Embedding; Cognitive Process; Protective Action Decision Model |
| Abstract |
Microblogging platforms have been increasingly used by the public and crisis managers in crisis. The increasing volume of data has made such platforms more difficult for officials to find on-the-ground information and understand the public’s perception of the evolving risks. The crisis informatics literature has proposed various technological solutions to find relevant information from social media. However, the cognitive processes of the affected population and their subsequent responses, such as perceptions, emotional and behavioral responses, are still under-examined at scale. Yet, such information is important for gauging public perception of risks, an important task for PIOs and emergency managers. In this work, we leverage the noise-cutting power of collective attention and take cues from the Protective Action Decision Model, to propose a method that estimates shifts in collective attention with a special focus on the cognitive processes of those affected and their subsequent responses. |
| Address |
University of Utah, School of Computing; University of Utah, School of Computing |
| 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/IMVX7820 |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2542 |
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| Author |
Justin Michael Crow |
| Title |
Verifying Baselines for Crisis Event Information Classification on Twitter |
Type |
Conference Article |
| Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
| Volume |
|
Issue |
|
Pages |
670-687 |
| Keywords |
Event-Detection, Social-Media, Crisis-Informatics, Word-Embeddings, CNN. |
| Abstract |
Social media are rich information sources during crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained to assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it's rare that code, trained models, or exhaustive parametrisation details are openly available. Thus, even confirming self-reported performance is problematic; authoritatively determining state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper makes 3 contributions: 1) replication and results confirmation of a leading technique; 2) testing straightforward modifications likely to improve performance; and 3) extension to a novel complimentary type of crisis-relevant information to demonstrate it's generalisability. |
| Address |
TAG-lab, University of Sussex |
| Corporate Author |
|
Thesis |
|
| Publisher |
Virginia Tech |
Place of Publication |
Blacksburg, VA (USA) |
Editor |
Amanda Hughes; Fiona McNeill; Christopher W. Zobel |
| Language |
English |
Summary Language |
English |
Original Title |
|
| Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
| Series Volume |
|
Series Issue |
|
Edition |
|
| ISSN |
978-1-949373-27-62 |
ISBN |
2411-3448 |
Medium |
|
| Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
| Notes |
jmcrow@protonmail.com |
Approved |
no |
| Call Number |
|
Serial |
2263 |
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