mlsanityData to 7 Oct 2026

Supervised Contrastive Learning for Multimodal Unreliable News Detection in COVID-19 Pandemic

arXiv 2109.01850 ↗First posted 4 Sep 2021cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 17 of its 29 references that are in the map. First posted on arXiv 4 Sep 2021.

Builds on

17 of 29 references.

References · 17

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5 more
  1. 2018A Survey on Natural Language Processing for Fake News Detectioncited by 67
  2. 2020ReCOVery: A Multimodal Repository for COVID-19 News Credibility Researchcited by 44
  3. 2019Combating Fake News: A Survey on Identification and Mitigation Techniquescited by 33
  4. 2017Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detectioncited by 25
  5. 2021Hierarchical Multi-head Attentive Network for Evidence-aware Fake News Detectioncited by 17

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Cited by year

Citing papers per year: 2023: 2.

2 papers.

  1. 2023UniS-MMC: Multimodal Classification via Unimodality-supervised Multimodal Contrastive Learningcited by 13
  2. 2023Joint Self-Supervised and Supervised Contrastive Learning for Multimodal MRI Data: Towards Predicting Abnormal Neurodevelopmentcited by 3