| Records |
| Author |
Cody Buntain; Richard Mccreadie; Ian Soboroff |
| Title |
Incident Streams 2020: TRECIS in the Time of COVID-19 |
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 |
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Issue |
|
Pages |
621-639 |
| Keywords |
Emergency Management, Crisis Informatics, Twitter, Categorization, Prioritization, COVID-19 |
| Abstract |
Between 2018 and 2019, the Incident Streams track (TREC-IS) has developed standard approaches for classifying the types and criticality of information shared in online social spaces during crises, but the introduction of SARS-CoV-2 has shifted the landscape of online crises substantially. While prior editions of TREC-IS have lacked data on large-scale public-health emergencies as these events are exceedingly rare, COVID-19 has introduced an over-abundance of potential data, and significant open questions remain about how existing approaches to crisis informatics and datasets built on other emergencies adapt to this new context. This paper describes how the 2020 edition of TREC-IS has addressed these dual issues by introducing a new COVID-19-specific task for evaluating generalization of existing COVID-19 annotation and system performance to this new context, applied to 11 regions across the globe. TREC-IS has also continued expanding its set of target crises, adding 29 new events and expanding the collection of event types to include explosions, fires, and general storms, making for a total of 9 event types in addition to the new COVID-19 events. Across these events, TREC-IS has made available 478,110 COVID-related messages and 282,444 crisis-related messages for participant systems to analyze, of which 14,835 COVID-related and 19,784 crisis-related messages have been manually annotated. Analyses of these new datasets and participant systems demonstrate first that both the distributions of information type and priority of information vary between general crises and COVID-19-related discussion. Secondly, despite these differences, results suggest leveraging general crisis data in the COVID-19 context improves performance over baselines. Using these results, we provide guidance on which information types appear most consistent between general crises and COVID-19. |
| Address |
New Jersey Institute of Technology; University of Glasgow; National Institute of Standards and Technology |
| Corporate Author |
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Thesis |
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| 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 |
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Medium |
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| Track |
Social Media for Disaster Response and Resilience |
Expedition |
|
Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
cbuntain@njit.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2360 |
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| Author |
Apoorva Chauhan; Amanda Hughes |
| Title |
COVID-19 Named Resources on Facebook, Twitter, and Reddit |
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 |
679-690 |
| Keywords |
Crisis Named Resources, Facebook, Twitter, Reddit, COVID-19 |
| Abstract |
Crisis Named Resources (CNRs) are social media accounts and pages named after a crisis event. They are created soon after an event occurs. CNRs share a lot of information around an event and are followed by many. In this study, we identify CNRs created around COVID-19 on Facebook, Twitter, and Reddit. We analyze when these resources were created, why they were created, how they were received by members of the public, and who created them. We conclude by comparing CNRs created around COVID-19 with past crisis events and discuss how CNR owners attempt to manage content and combat misinformation. |
| Address |
University of Waterloo; Brigham Young University |
| Corporate Author |
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Thesis |
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| 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 |
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Medium |
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| Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
apoorva.chauhan@aggiemail.usu.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2364 |
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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 |
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Thesis |
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| 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 |
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Medium |
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| 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 |
Antone Evans Jr.; Yingyuan Yang; Sunshin Lee |
| Title |
Towards Predicting COVID-19 Trends: Feature Engineering on Social Media Responses |
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 |
792-807 |
| Keywords |
Big Data Analysis, Machine Learning, COVID-19, Twitter |
| Abstract |
During the course of this pandemic, the use of social media and virtual networks has been at an all-time high. Individuals have used social media to express their thoughts on matters related to this pandemic. It is difficult to predict current trends based on historic case data because trends are more connected to social activities which can lead to the spread of coronavirus. So, it's important for us to derive meaningful information from social media as it is widely used. Therefore, we grouped tweets by common keywords, found correlations between keywords and daily COVID-19 statistics and built predictive modeling. The features correlation analysis was very effective, so trends were predicted very well. A RMSE score of 0.0425504, MAE of 0.03295105 and RSQ of 0.5237014 in relation to daily deaths. In addition, we found a RMSE score of 0.07346836, MAE of 0.0491152 and RSQ 0.374529 in relation to daily cases. |
| Address |
University of Illinois Springfield; University of Illinois Springfield; University of Illinois Springfield |
| Corporate Author |
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Thesis |
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| 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 |
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Medium |
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| Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
aevan7@uis.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2374 |
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| Author |
Shivam Sharma; Cody Buntain |
| Title |
An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
|
Issue |
|
Pages |
808-815 |
| Keywords |
covid19, twitter, trecis, cross-validation, machine learning, transfer learning |
| Abstract |
In 2020, we have witnessed an unprecedented crisis event, the COVID-19 pandemic. Various questions arise regarding the nature of this crisis data and the impacts it would have on the existing tools. In this paper, we aim to study whether we can include pandemic-type crisis events with general non-pandemic events and hypothesize that including labeled crisis data from a variety of non-pandemic events will improve classification performance over models trained solely on pandemic events. To test our hypothesis we study the model performance for different models by performing a cross validation test on pandemic only held-out sets for two different types of training sets, one containing only pandemic data and the other a combination of pandemic and non-pandemic crisis data, and comparing the results of the two. Our results approve our hypothesis and give evidence of some crucial information propagation upon inclusion of non-pandemic crisis data to pandemic data. |
| Address |
New Jersey Institute of Technology; New Jersey Institute of Technology |
| Corporate Author |
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Thesis |
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| 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 |
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Medium |
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| Track |
Social Media for Disaster Response and Resilience |
Expedition |
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Conference |
18th International Conference on Information Systems for Crisis Response and Management |
| Notes |
cbuntain@njit.edu |
Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2375 |
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