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Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic
30th ACM International Conference on Information and Knowledge Management, CIKM 2021 ; : 3637-3641, 2021.
Article in English | Scopus | ID: covidwho-1528571
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. © 2021 ACM.

Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 30th ACM International Conference on Information and Knowledge Management, CIKM 2021 Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 30th ACM International Conference on Information and Knowledge Management, CIKM 2021 Year: 2021 Document Type: Article