Alrashdi, Reem and O'Keefe, Simon orcid.org/0000-0001-5957-2474 (2019) Robust Domain Adaptation Approach for Tweet Classification for Crisis Response. In: EMENA-ISTL 2019: Innovation in Information Systems and Technologies to Support Learning Research. Springer , pp. 124-134.
Abstract
Information posted by people on Twitter during crises can significantly improve crisis response towards reducing human and financial loss. Deep learning algorithms can identify related tweets to reduce information overloaded which prevents humanitarian organizations from using Twitter posts. However, they heavily rely on labeled data which is unavailable for emerging crises. And because each crisis has its own features such as location, occurring time and social media response, current models are known to suffer from generalizing to unseen disaster events when pretrained on past ones. To solve this problem, we propose a domain adaptation approach that makes use of a distant supervision-based framework to label the unlabeled data from emerging crises. Then, pseudo-labeled target data, along with labeled-data from similar past disasters, are used to build the target model. Our results show that our approach can be seen as a general robust method to classify unseen tweets from current events.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer Nature Switzerland AG 2020. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details. |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 26 Nov 2021 12:10 |
Last Modified: | 07 Feb 2025 00:08 |
Published Version: | https://doi.org/10.1007/978-3-030-36778-7_14 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/978-3-030-36778-7_14 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180896 |
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Description: Robust Domain Adaptation Approach for Tweet Classification for Crisis Response