Alrashdi, Reem and O'Keefe, Simon orcid.org/0000-0001-5957-2474 (2020) Automatic Labeling of Tweets for Crisis Response Using Distant Supervision. In: WWW '20: Companion Proceedings of the Web Conference 2020. ACM , pp. 418-425.
Abstract
Current tweet classification models aimed at enhancing crisis response are based on supervised deep learning. They rely on the quality and quantity of human-labeled training data. Still, the available training data is small in size and imbalanced in coverage of crisis types, which prevents the models from generalization, and as it is manually labeled, it is also expensive to produce. To overcome these problems, distant supervision can be applied to automatically generate large-scale labeled data for tweet classification for crisis response. Experimental results on different crisis events show that our work can produce good quality labeled data from past and recent events. Substituting automatically labeled training data for part of the manually labeled training data has a minimal impact on the model performance, indicating that automatically labeled data can be used when no hand-labeled data is available.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IW3C2 (International World Wide Web Conference Committee) |
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: | 25 Nov 2021 09:50 |
Last Modified: | 05 Mar 2025 00:10 |
Published Version: | https://doi.org/10.1145/3366424.3383757 |
Status: | Published |
Publisher: | ACM |
Identification Number: | 10.1145/3366424.3383757 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:180856 |
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Filename: 3366424.3383757_1_.pdf
Description: Automatic Labeling of Tweets for Crisis Response Using Distant Supervision
Licence: CC-BY 2.5