| Records |
| Author |
Shivam Sharma; Cody Buntain |
| Title |
An Evaluation of Twitter Datasets from Non-Pandemic Crises Applied to Regional COVID-19 Contexts |
Type |
Conference Article |
| Year |
2021 |
Publication |
ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2021 |
| Volume |
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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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| Author |
Shivam Sharma; Cody Buntain |
| Title |
Bang for your Buck: Performance Impact Across Choices in Learning Architectures for Crisis Informatics |
Type |
Conference Article |
| Year |
2022 |
Publication |
ISCRAM 2022 Conference Proceedings – 19th International Conference on Information Systems for Crisis Response and Management |
Abbreviated Journal |
Iscram 2022 |
| Volume |
|
Issue |
|
Pages |
719-736 |
| Keywords |
Incident Streams; TREC; TRECIS; crisis informatics |
| Abstract |
Over the years, with the increase in social media engagement, there has been an in increase in various pipelines to analyze, classify and prioritize crisis-related data on various social media platforms. These pipelines utilize various data augmentation methods to counter imbalanced crisis data, sophisticated and off-the-shelf models for training. However, there is a lack of comprehensive study which compares these methods for the various sections of a pipeline. In this study, we split a general crisis-related pipeline into 3 major sections, namely, data augmentation, model selection, and training methodology. We compare various methods for each of these sections and then present a comprehensive evaluation of which section to prioritize based on the results from various pipelines. We compare our results against two separate tasks, information classification and priority scoring for crisis-related tweets. Our results suggest that data augmentation, in general,improves the performance. However, sophisticated, state-of-the-art language models like DeBERTa only show performance gain in information classification tasks, and models like RoBERTa tend to show a consistent performance increase over our presented baseline consisting of BERT. We also show that, though training two separate task-specific BERT models does show better performance than one BERT model with multi-task learning methodology over an imbalanced dataset, multi-task learning does improve performance for more sophisticated model like DeBERTa with a much more balanced dataset after augmentation. |
| Address |
New Jersey Institute of Technology; New Jersey Institute of Technology |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
Tarbes, France |
Editor |
Rob Grace; Hossein Baharmand |
| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
2411-3387 |
ISBN |
978-82-8427-099-9 |
Medium |
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| Track |
Social Media for Crisis Management |
Expedition |
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Conference |
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| Notes |
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Approved |
no |
| Call Number |
ISCRAM @ idladmin @ |
Serial |
2451 |
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