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Song, X., Petrak, J. orcid.org/0000-0001-8038-3096, Jiang, Y. et al. (3 more authors) (Submitted: 2020) Classification aware neural topic model and its application on a new COVID-19 disinformation corpus. arXiv. (Submitted)
Abstract
The explosion of disinformation related to the COVID-19 pandemic has overloaded fact-checkers and media worldwide. To help tackle this, we developed computational methods to support COVID-19 disinformation debunking and social impacts research. This paper presents: 1) the currently largest available manually annotated COVID-19 disinformation category dataset; and 2) a classification-aware neural topic model (CANTM) that combines classification and topic modelling under a variational autoencoder framework. We demonstrate that CANTM efficiently improves classification performance with low resources, and is scalable. In addition, the classification-aware topics help researchers and end-users to better understand the classification results.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 The Author(s). For reuse permissions, please contact the Author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 Aug 2020 06:43 |
Last Modified: | 24 Aug 2020 07:22 |
Published Version: | https://arxiv.org/abs/2006.03354v1 |
Status: | Submitted |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164746 |
Available Versions of this Item
- Classification aware neural topic model and its application on a new COVID-19 disinformation corpus. (deposited 24 Aug 2020 06:43) [Currently Displayed]