Singh, I., Bontcheva, K. orcid.org/0000-0001-6152-9600, Song, X. et al. (1 more author) (2022) Comparative analysis of engagement, themes, and causality of Ukraine-related debunks and disinformation. In: Hopfgartner, F., Jaidka, K., Mayr, P., Jose, J. and Breitsohl, J., (eds.) Social Informatics: 13th International Conference, SocInfo 2022, Glasgow, UK, October 19–21, 2022, Proceedings. 13th International Conference, SocInfo 2022, 19-21 Oct 2022, Glasgow, UK. Lecture Notes in Computer Science . Springer International Publishing , pp. 128-143. ISBN 9783031190964
Abstract
This paper compares quantitatively the spread of Ukraine-related disinformation and its corresponding debunks, first by considering re-tweets, replies, and favourites, which demonstrate that despite platform efforts Ukraine-related disinformation is still spreading wider than its debunks. Next, bidirectional post-hoc analysis is carried out using Granger causality tests, impulse response analysis and forecast error variance decomposition, which demonstrate that the spread of debunks has a positive impact on reducing Ukraine-related disinformation eventually, albeit not instantly. Lastly, the paper investigates the dominant themes in Ukraine-related disinformation and their spatiotemporal distribution. With respect to debunks, we also establish that around 18% of fact-checks are debunking claims which have already been fact-checked in another language. The latter finding highlights an opportunity for better collaboration between fact-checkers, so they can benefit from and amplify each other’s debunks through translation, citation, and early publication online.
Metadata
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Copyright, Publisher and Additional Information: | © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG. This is an author-produced version of a paper subsequently published in Lecture Notes in Computer Science. Uploaded in accordance with the publisher's self-archiving policy. | ||||||
Keywords: | Disinformation; Debunks; Ukraine-related disinformation; Comparative analysis; Social media | ||||||
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Institution: | The University of Sheffield | ||||||
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) | ||||||
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Depositing User: | Symplectic Sheffield | ||||||
Date Deposited: | 13 Dec 2022 16:57 | ||||||
Last Modified: | 12 Oct 2023 00:13 | ||||||
Status: | Published | ||||||
Publisher: | Springer International Publishing | ||||||
Series Name: | Lecture Notes in Computer Science | ||||||
Refereed: | Yes | ||||||
Identification Number: | https://doi.org/10.1007/978-3-031-19097-1_8 |