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Song, X., Petrak, J. orcid.org/0000-0001-8038-3096, Jiang, Y. et al. (3 more authors) (2021) Classification aware neural topic model for COVID-19 disinformation categorisation. PLoS ONE, 16 (2). e0247086. ISSN 1932-6203
Abstract
The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation. The COVID-19 disinformation categories could be used for a) focusing fact-checking efforts on the most damaging kinds of COVID-19 disinformation; b) guiding policy makers who are trying to deliver effective public health messages and counter effectively COVID-19 disinformation. This paper presents: 1) a corpus containing what is currently the largest available set of manually annotated COVID-19 disinformation categories; 2) a classification-aware neural topic model (CANTM) designed for COVID-19 disinformation category classification and topic discovery; 3) an extensive analysis of COVID-19 disinformation categories with respect to time, volume, false type, media type and origin source.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | COVID-19; Classification; Communication; Data Curation; Neural Networks, Computer |
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) |
Funding Information: | Funder Grant number European Commission - HORIZON 2020 825297 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 16 Mar 2021 14:35 |
Last Modified: | 02 Dec 2022 17:13 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0247086 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:172012 |
Available Versions of this Item
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Classification aware neural topic model and its application on a new COVID-19 disinformation corpus. (deposited 24 Aug 2020 06:43)
- Classification aware neural topic model for COVID-19 disinformation categorisation. (deposited 16 Mar 2021 14:35) [Currently Displayed]
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