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Supervised contrastive learning for multimodal unreliable news detection in COVID-19 pandemic
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Zhang, Wenjian, Gui, Lin and He, Yulan (2021) Supervised contrastive learning for multimodal unreliable news detection in COVID-19 pandemic. In: 30th ACM International Conference on Information and Knowledge Management, Virtual conference, 01-05 Nov 2021. Published in: CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management pp. 3637-3641. doi:10.1145/3459637.3482196
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WRAP-supervised-contrastive-learning-multimodal-unreliable-news-detection-COVID-19-pandemic-AAM-Zhang-2021.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (4Mb) |
Official URL: https://doi.org/10.1145/3459637.3482196
Abstract
As the digital news industry becomes the main channel of information dissemination, the adverse impact of fake news is explosively magnified. The credibility of a news report should not be considered in isolation. Rather, previously published news articles on the similar event could be used to assess the credibility of a news report. Inspired by this, we propose a BERT-based multimodal unreliable news detection framework, which captures both textual and visual information from unreliable articles utilising the contrastive learning strategy. The contrastive learner interacts with the unreliable news classifier to push similar credible news (or similar unreliable news) closer while moving news articles with similar content but opposite credibility labels away from each other in the multimodal embedding space. Experimental results on a COVID-19 related dataset, ReCOVery, show that our model outperforms a number of competitive baseline in unreliable news detection.
Item Type: | Conference Item (Paper) | |||||||||||||||
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Subjects: | P Language and Literature > PN Literature (General) Q Science > Q Science (General) Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||
Library of Congress Subject Headings (LCSH): | COVID-19 Pandemic, 2020- , COVID-19 Pandemic, 2020- -- Data processing, Data mining , Data mining -- Health aspects, Fake news -- Data processing, Supervised learning (Machine learning) | |||||||||||||||
Journal or Publication Title: | CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management | |||||||||||||||
Publisher: | ACM | |||||||||||||||
Official Date: | 30 October 2021 | |||||||||||||||
Dates: |
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Page Range: | pp. 3637-3641 | |||||||||||||||
DOI: | 10.1145/3459637.3482196 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 1 September 2021 | |||||||||||||||
Date of first compliant Open Access: | 1 September 2021 | |||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||
Title of Event: | 30th ACM International Conference on Information and Knowledge Management | |||||||||||||||
Type of Event: | Conference | |||||||||||||||
Location of Event: | Virtual conference | |||||||||||||||
Date(s) of Event: | 01-05 Nov 2021 | |||||||||||||||
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