ArticleSustainable cities and society2021
Fake news detection for epidemic emergencies via deep correlations between text and images.
Article in Sustainable cities and society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Fake news during the war in Ukraine: coping strategies and fear of war in the general population of Romania and in aid workers.Frontiers in psychology · 2023Article
- An image and text-based multimodal model for detecting fake news in OSN's.Journal of intelligent information systems · 2022Article
- How Does Social Media Influence People to Get Vaccinated? The Elaboration Likelihood Model of a Person's Attitude and Intention to Get COVID-19 Vaccines.International journal of environmental research and public health · 2022Article
- Content characteristics predict the putative authenticity of COVID-19 rumors.Frontiers in public health · 2022Article
- Combating multimodal fake news on social media: methods, datasets, and future perspective.Multimedia systems · 2022Article
- Internet Rumors During the COVID-19 Pandemic: Dynamics of Topics and Public Psychologies.Frontiers in public health · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, major emergencies have occurred frequently all over the world. When a major global public heath emergency like COVID-19 broke out, an increasing number of fake news in social media networks are exposed to the public. Automatically detecting the veracity of a news article ensures people receive truthful information, which is beneficial to the epidemic prevention and control. However, most of the existing fake news detection methods focus on inferring clues from text-only content, which ignores the semantic correlations across multimodalities. In this work, we propose a novel approach for Fake News Detection by comprehensively mining the Semantic Correlations between Text content and Images attached (FND-SCTI). First, we learn image representations via the pretrained VGG model, and use them to enhance the learning of text representation via hierarchical attention mechanism. Second, a multimodal variational autoencoder is exploited to learn a fused representation of textual and visual content. Third, the image-enhanced text representation and the multimodal fusion eigenvector are combined to train the fake news detector. Experimental results on two real-world fake news datasets, Twitter and Weibo, demonstrate that our model outperforms seven competitive approaches, and is able to capture the semantic correlations among multimodal contents.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.