Records |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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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 |
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 |
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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 |
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 |
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Conference |
|
Notes |
http://dx.doi.org/10.59297/YHDI4576 |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2536 |
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Author |
Jeremy Diaz; Lise St. Denis; Maxwell B. Joseph; Kylen Solvik; Jennifer K. Balch |
Title |
Classifying Twitter Users for Disaster Response: A Highly Multimodal or Simple Approach? |
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 |
774-789 |
Keywords |
User Classification, Disaster Response, Twitter, Model Comparison, Multimodal Deep Learning. |
Abstract |
We report on the development of a classifier to identify Twitter users contributing first-hand information during a disaster. Identifying such users helps social media monitoring teams identify critical information that might otherwise slip through the cracks. A parallel study (St. Denis et al., 2020) demonstrates that Twitter user filtering creates an information-rich stream of content, but the best way to approach this task is unexplored. A user's profile contains many different “modalities” of data, including numbers, text, and images. To integrate these different data types, we constructed a multimodal neural network that combines the loss function of all modalities, and we compared the results to many individual unimodal models and a decision-level fusion approach. Analysis of the results suggests that unimodal models acting on Twitter users' recent tweets are sufficient for accurate classification. We demonstrate promising classification of Twitter users for crisis response with methods that are (1) easy to implement and (2) quick to both optimize and infer. |
Address |
Institute for Computational and Data Sciences, The Penn State University Department of Geography, The Penn State University; CIRES, Earth Lab, University of Colorado, Boulder; CIRES, Earth Lab, University of Colorado, Boulder; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder |
Corporate Author |
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Thesis |
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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 |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-27-69 |
ISBN |
2411-3455 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
jad6655@psu.edu |
Approved |
no |
Call Number |
|
Serial |
2270 |
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Author |
James A. Reep; Andrea Tapia |
Title |
Toward an Organizational Technology Adoption Process (OTAP) for Social Media Integration in a PSAP |
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 |
718-729 |
Keywords |
Crisis informatics, Organizational Change, Technology Adoption, Social Media, OTAP |
Abstract |
Integration of social media in emergency response environments presents specific organizational challenges, such as lack of resources or information credibility. Additionally, there exists individual resistance to change in these environments that could potentially discourage adoption. To identify and understand these challenges, we conducted semi-structured group interviews with emergency call takers and dispatchers. We find that these PSAP operators desire participation and explanation of changes throughout the organizational change process. Participants also articulated they desired training regarding change even when not directly affected. Though change management procedures often call for these strategies, they are commonly overlooked, leaving individuals to imagine worse case scenarios that manifest as additional stress in an already stressful work environment. It is suggested that a formalized change management process which directly addresses the identified challenges within the organizational technology adoption process (OTAP) is needed in order to mitigate undue stress. |
Address |
The Pennsylvania State University; The Pennsylvania State University |
Corporate Author |
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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 |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-27-65 |
ISBN |
2411-3451 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
jar5757@psu.edu |
Approved |
no |
Call Number |
|
Serial |
2266 |
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|
Author |
Jens Kersten; Jan Bongard; Friederike Klan |
Title |
Combining Supervised and Unsupervised Learning to Detect and Semantically Aggregate Crisis-Related Twitter Content |
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 |
744-754 |
Keywords |
Information Overload Reduction, Semantic Clustering, Crisis Informatics, Twitter Stream |
Abstract |
Twitter is an immediate and almost ubiquitous platform and therefore can be a valuable source of information during disasters. Current methods for identifying and classifying crisis-related content are often based on single tweets, i.e., already known information from the past is neglected. In this paper, the combination of tweet-wise pre-trained neural networks and unsupervised semantic clustering is proposed and investigated. The intention is to (1) enhance the generalization capability of pre-trained models, (2) to be able to handle massive amounts of stream data, (3) to reduce information overload by identifying potentially crisis-related content, and (4) to obtain a semantically aggregated data representation that allows for further automated, manual and visual analyses. Latent representations of each tweet based on pre-trained sentence embedding models are used for both, clustering and tweet classification. For a fast, robust and time-continuous processing, subsequent time periods are clustered individually according to a Chinese restaurant process. Clusters without any tweet classified as crisis-related are pruned. Data aggregation over time is ensured by merging semantically similar clusters. A comparison of our hybrid method to a similar clustering approach, as well as first quantitative and qualitative results from experiments with two different labeled data sets demonstrate the great potential for crisis-related Twitter stream analyses. |
Address |
German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Department; German Aerospace Center (DLR), Institute of Data Science, Citizen Science Departmen |
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 |
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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 |
jens.kersten@dlr.de |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2369 |
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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 |
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Abbreviated Series Title |
|
Series Volume |
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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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Author |
Lise Ann St. Denis; Amanda Lee Hughes; Jeremy Diaz; Kylen Solvik; Maxwell B. Joseph; Jennifer K. Balch |
Title |
'What I Need to Know is What I Don't Know!': Filtering Disaster Twitter Data for Information from Local Individuals |
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 |
730-743 |
Keywords |
Crisis Informatics, Social Media, Emergency Management, Situational Awareness. |
Abstract |
We report on the design, development, and evaluation of a user labeling framework for social media monitoring by emergency responders. By labeling Twitter user accounts based on behavior and content, this novel approach identifies tweets from accounts belonging to Individuals generating Personalized content and captures information that might otherwise be missed. We evaluate the framework using training data from the 2018 Camp, Woolsey, and Hill fires. Approximately 30% of the Individual-Personalized tweets contain first-hand information, providing a rich stream of content for social media monitoring. Because it can quickly eliminate most redundant tweets, this framework could be a critical first step in an end-to-end information extraction pipeline. It may also generalize more easily for new disaster events since it relies on general user account attributes rather than tweet content. We conclude with next steps for refining and evaluating our framework in near real-time during a disaster response. |
Address |
CIRES, Earth Lab, University of Colorado, Boulder; Crisis Informatics Lab Brigham Young University; Institute for Computational and Data Sciences, Department of Geography, Penn State University; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder; CIRES, Earth Lab, University of Colorado, Boulder; CIRES, Earth Lab, Department of Geography, University of Colorado, Boulder |
Corporate Author |
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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 |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-27-66 |
ISBN |
2411-3452 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
Lise.St.Denis@Colorado.edu |
Approved |
no |
Call Number |
|
Serial |
2267 |
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Author |
Liuqing Li; Edward A. Fox |
Title |
Disaster Response Patterns across Different User Groups on Twitter: A Case Study during Hurricane Dorian |
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 |
838-848 |
Keywords |
Hurricane, Response, Pattern, User Classification, Twitter |
Abstract |
We conducted a case study analysis of disaster response patterns across different user groups during Hurricane Dorian in 2019. We built a tweet collection about the hurricane, covering a two week period. We divided Twitter users into two groups: brand/organization or individual. We found a significant difference in response patterns between the groups. Brand users increasingly participated as the disaster unfolded, and they posted more tweets than individual users on average. Regarding emotions, brand users posted more tweets with joy and surprise, while individual users posted more tweets with sadness. Fear was a common emotion between the two groups. Further, both groups used different types of hashtags and words in their tweets. Some distinct patterns were also discovered in their concerns on specific topics. These results suggest the value of further exploration with more tweet collections, considering the behavior of different user groups during disasters. |
Address |
Department of Computer Science, Virginia Tech; Department of Computer Science, Virginia Tech; |
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 |
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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 |
978-1-949373-27-74 |
ISBN |
2411-3460 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
liuqing@vt.edu |
Approved |
no |
Call Number |
|
Serial |
2275 |
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Author |
Lucia Castro Herrera |
Title |
Configuring Social Media Listening Practices in Crisis Management |
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 |
640-654 |
Keywords |
Social media listening, Practice, Improvisation, Crisis management strategy, Configuration |
Abstract |
Social media listening practices are increasingly adopted in crisis management and have become an object of interest for researchers and practitioners alike. This article analyzes how these enactments have been studied in the academic literature. Through a systematic review of the available body of knowledge, features from studies involving depictions of practice were extracted, analyzed, and turned into a narrative using an inductive approach. Strategies of improvisation, overreliance on personal and professional networks, manual work, spontaneous coordination, and re-assigning tasks represent the main findings in the multidisciplinary literature. This article is a consolidated overview of experiences from social media listening in practice beyond listing the benefits of social media as a source of information. Moreover, the paper sets the basis for future studies on the range of possible configurations and institutionalization of disruptive crisis management practices. |
Address |
Universitetet i Agder |
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 |
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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 |
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 |
lucia.c.herrera@uia.no |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2361 |
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Author |
Matti Wiegmann; Jens Kersten; Friederike Klan; Martin Potthast; Benno Stein |
Title |
Analysis of Detection Models for Disaster-Related Tweets |
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 |
872-880 |
Keywords |
Tweet Filtering; Crisis Management; Evaluation Framework |
Abstract |
Social media is perceived as a rich resource for disaster management and relief efforts, but the high class imbalance between disaster-related and non-disaster-related messages challenges a reliable detection. We analyze and compare the effectiveness of three state-of-the-art machine learning models for detecting disaster-related tweets. In this regard we introduce the Disaster Tweet Corpus~2020, an extended compilation of existing resources, which comprises a total of 123,166 tweets from 46~disasters covering 9~disaster types. Our findings from a large experiments series include: detection models work equally well over a broad range of disaster types when being trained for the respective type, a domain transfer across disaster types leads to unacceptable performance drops, or, similarly, type-agnostic classification models behave more robust at a lower effectiveness level. Altogether, the average misclassification rate of~3,8\% on performance-optimized detection models indicates effective classification knowledge but comes at the price of insufficient generalizability. |
Address |
Bauhaus-Universit\“at Weimar German Aerospace Center (DLR); German Aerospace Center (DLR); German Aerospace Center (DLR); Leipzig University; Bauhaus-Universit\”at Weimar |
Corporate Author |
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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 |
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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 |
978-1-949373-27-77 |
ISBN |
2411-3463 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
matti.wiegmann@uni-weimar.de |
Approved |
no |
Call Number |
|
Serial |
2278 |
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Author |
Muhammad Imran; Firoj Alam; Umair Qazi; Steve Peterson; Ferda Ofli |
Title |
Rapid Damage Assessment Using Social Media Images by Combining Human and Machine Intelligence |
Type |
Conference Article |
Year |
2020 |
Publication |
ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2020 |
Volume |
|
Issue |
|
Pages |
761-773 |
Keywords |
Social Media, Damage Assessment, Artificial Intelligence, Image Processing. |
Abstract |
Rapid damage assessment is one of the core tasks that response organizations perform at the onset of a disaster to understand the scale of damage to infrastructures such as roads, bridges, and buildings. This work analyzes the usefulness of social media imagery content to perform rapid damage assessment during a real-world disaster. An automatic image processing system, which was activated in collaboration with a volunteer response organization, processed ~280K images to understand the extent of damage caused by the disaster. The system achieved an accuracy of 76% computed based on the feedback received from the domain experts who analyzed ~29K system-processed images during the disaster. An extensive error analysis reveals several insights and challenges faced by the system, which are vital for the research community to advance this line of research. |
Address |
Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar; Montgomery County, Maryland Community Emergency Response Team United States; Qatar Computing Research Institute Hamad Bin Khalifa University Doha, Qatar |
Corporate Author |
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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 |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
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Edition |
|
ISSN |
978-1-949373-27-68 |
ISBN |
2411-3454 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
mimran@hbku.edu.qa |
Approved |
no |
Call Number |
|
Serial |
2269 |
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Author |
Nasik Muhammad Nafi; Avishek Bose; Sarthak Khanal; Doina Caragea; William H. Hsu |
Title |
Abstractive Text Summarization of Disaster-Related Documents |
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 |
881-892 |
Keywords |
Disaster Reporting; Text Summarization; Information Extraction; Reinforcement Learning; Evaluation Metrics |
Abstract |
Abstractive summarization is intended to capture key information from the full text of documents. In the application domain of disaster and crisis event reporting, key information includes disaster effects, cause, and severity. While some researches regarding information extraction in the disaster domain have focused on keyphrase extraction from short disaster-related texts like tweets, there is hardly any work that attempts abstractive summarization of long disaster-related documents. Following the recent success of Reinforcement Learning (RL) in other domains, we leverage an RL-based state-of-the-art approach in abstractive summarization to summarize disaster-related documents. RL enables an agent to find an optimal policy by maximizing some reward. We design a novel hybrid reward metric for the disaster domain by combining \underline{Vec}tor Similarity and \underline{Lex}icon Matching (\textit{VecLex}) to maximize the relevance of the abstract to the source document while focusing on disaster-related keywords. We evaluate the model on a disaster-related subset of a CNN/Daily Mail dataset consisting of 104,913 documents. The results show that our approach produces more informative summaries and achieves higher \textit{VecLex} scores compared to the baseline. |
Address |
Kansas State University; Kansas State University; Kansas State University; Kansas State University; Kansas State University |
Corporate Author |
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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 |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
|
Series Issue |
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Edition |
|
ISSN |
978-1-949373-27-78 |
ISBN |
2411-3464 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
nnafi@ksu.edu |
Approved |
no |
Call Number |
|
Serial |
2279 |
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Author |
Nilani Algiriyage; Rangana Sampath; Raj Prasanna; Kristin Stock; Emma Hudson-Doyle; David Johnston |
Title |
Identifying Disaster-related Tweets: A Large-Scale Detection Model Comparison |
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 |
731-743 |
Keywords |
Tweet Classification, Machine Learning, Deep Learning, Disasters |
Abstract |
Social media applications such as Twitter and Facebook are fast becoming a key instrument in gaining situational awareness (understanding the bigger picture of the situation) during disasters. This has provided multiple opportunities to gather relevant information in a timely manner to improve disaster response. In recent years, identifying crisis-related social media posts is analysed as an automatic task using machine learning (ML) or deep learning (DL) techniques. However, such supervised learning algorithms require labelled training data in the early hours of a crisis. Recently, multiple manually labelled disaster-related open-source twitter datasets have been released. In this work, we create a large dataset with 186,718 tweets by combining a number of such datasets and evaluate the performance of multiple ML and DL algorithms in classifying disaster-related tweets in three settings, namely ``in-disaster'', ``out-disaster'' and ``cross-disaster''. Our results show that the Bidirectional LSTM model with Word2Vec embeddings performs well for the tweet classification task in all three settings. We also make available the preprocessing steps and trained weights for future research. |
Address |
Massey University; Massey University; Massey University; Massey University; Joint Centre for Disaster Research, Massey University; Joint Center of Disaster Research, Massey University Wellington |
Corporate Author |
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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 |
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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 |
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 |
rangika.nilani@gmail.com |
Approved |
no |
Call Number |
ISCRAM @ idladmin @ |
Serial |
2368 |
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|
Author |
Richard McCreadie; Cody Buntain; Ian Soboroff |
Title |
Incident Streams 2019: Actionable Insights and How to Find Them |
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 |
744-760 |
Keywords |
Emergency Management, Crisis Informatics, Real-time, Twitter, Categorization. |
Abstract |
The ubiquity of mobile internet-enabled devices combined with wide-spread social media use during emergencies is posing new challenges for response personnel. In particular, service operators are now expected to monitor these online channels to extract actionable insights and answer questions from the public. A lack of adequate tools makes this monitoring impractical at the scale of many emergencies. The TREC Incident Streams (TREC-IS) track drives research into solving this technology gap by bringing together academia and industry to develop techniques for extracting actionable insights from social media streams during emergencies. This paper covers the second year of TREC-IS, hosted in 2019 with two editions, 2019-A and 2019-B, contributing 12 new events and approximately 20,000 new tweets across 25 information categories, with 15 research groups participating across the world. This paper provides an overview of these new editions, actionable insights from data labelling, and the automated techniques employed by participant systems that appear most effective. |
Address |
University of Glasgow; InfEco Lab, New Jersey Institute of Technology (NJIT); National Institute of Standards and Technology (NIST) |
Corporate Author |
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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 |
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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 |
|
ISSN |
978-1-949373-27-67 |
ISBN |
2411-3453 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
richard.mccreadie@glasgow.ac.uk |
Approved |
no |
Call Number |
|
Serial |
2268 |
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Author |
Rob Grace |
Title |
Hyperlocal Toponym Usage in Storm-Related Social Media |
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 |
849-859 |
Keywords |
Volunteered Geographic Information, Twitter, Information Behavior, Crisis Informatics, Emergency Management. |
Abstract |
Crisis responders need to locate events reported in social media messages that typically lack geographic metadata such as geotags. Toponyms, places names referenced in messages, provide another source of geographic information, however, the availability and granularity of toponyms in crisis social media remain poorly understood. This study examines toponym usage and granularity across six categories of crisis-related information posted on Twitter during a severe storm. Findings show users often include geographic information in messages describing local and remote storm events but do so rarely when discussing other topics, more often use toponyms than geotags when describing local events, and tend to include fine-grained toponyms in reports of infrastructure damage and service disruption and course-grained toponyms in other kinds of storm-related messages. These findings present requirements for hyperlocal geoparsing techniques and suggest that social media monitoring presents more immediate affordances for course-grained damage assessment than fine-grained situational awareness during a crisis. |
Address |
Texas Tech University |
Corporate Author |
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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 |
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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 |
|
ISSN |
978-1-949373-27-75 |
ISBN |
2411-3461 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
rob.grace@ttu.edu |
Approved |
no |
Call Number |
|
Serial |
2276 |
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Author |
Kamol Roy; MD Ashraf Ahmed; Samiul Hasan; Arif Mohaimin Sadri, P.D. |
Title |
Dynamics of Crisis Communications in Social Media: Spatio-temporal and Text-based Comparative Analyses of Twitter Data from Hurricanes Irma and Michael |
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 |
812-824 |
Keywords |
Social Media, Dynamic Topic Modeling, Irma, Michael, Disaster Management. |
Abstract |
Social media platforms play critical roles in information dissemination, communication and co-ordination during different phases of natural disasters as it is crucial to know the type of crisis information being disseminated and user concerns. Large-scale Twitter data from hurricanes Irma (Sept. 2017) and Michael (Oct. 2018) are used here to understand the topic dynamics over time by applying the Dynamic Topic Model, followed by a comparative analyses of the differences in such dynamics for these two hurricane scenarios. We performed a spatio-temporal analyses of user activities with reference to the hurricane center location and wind speed. The findings of spatio-temporal analyses show that differences in hurricane path and the affected regions influence user participation and social media activity. Besides, topic dynamics reveals that situational awareness, disruptions, relief action are among the patterns common for both hurricanes; unlike topics such as hurricane evacuation and political situation that are scenario dependent. |
Address |
Department of CECE University of Central Florida; Department of CECE University of Central Florida; Department of CECE University of Central Florida; Department of MDCM Florida International University |
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 |
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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 |
|
Series Issue |
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Edition |
|
ISSN |
978-1-949373-27-72 |
ISBN |
2411-3458 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
roy.kamol@knights.ucf.edu |
Approved |
no |
Call Number |
|
Serial |
2273 |
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Author |
Sandrine Bubendorff; Caroline Rizza |
Title |
The Wikipedia Contribution to Social Resilience During Terrorist Attacks |
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 |
790-801 |
Keywords |
Wikipedia, Resilience Process, Terrorist Attacks, Social Media. |
Abstract |
This paper aims at studying the role of Wikipedia in social resilience processes during terrorist attacks. It discusses how Wikipedia users' specific skills are mobilized in order to make sense of the event as it unfolds. We have conducted an ethnographic analysis of several Wikipedia's terrorist attacks pages as well as interviews with regular Wikipedia's contributors. We document how Wikipedia is used during crisis by readers and contributors. Doing so, we identify a specific pace of contributions which provides reliable information to readers. By discussing the conditions of their trustworthiness, we highlight how historical sources (i.e. traditional media and authorities) support this pace. Our analyses demonstrate that citizens are engaging very quickly in processes of resilience and should be, therefore, considered as relevant partners by authorities when engaging a response to the crisis. |
Address |
i3-SES, Telecom Paris, IP Paris, CNRS; i3-SES, Telecom Paris, IP Paris, CNRS |
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 |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
|
Series Volume |
|
Series Issue |
|
Edition |
|
ISSN |
978-1-949373-27-70 |
ISBN |
2411-3456 |
Medium |
|
Track |
Social Media for Disaster Response and Resilie |
Expedition |
|
Conference |
17th International Conference on Information Systems for Crisis Response and Management |
Notes |
sandrine.bubendorff@telecom-paristech.fr |
Approved |
no |
Call Number |
|
Serial |
2271 |
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