Modality and Event Adversarial Networks for Multi-Modal Fake News Detection

Article Computer Science, Information Systems

Detecting fake news stories via multimodal analysis

Vivek K. Singh et al.

Summary: This study proposes a multimodal approach combining text and visual analysis to automatically detect fake news, demonstrating the superiority of multimodal methods in fake news detection.

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY (2021)

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Article Computer Science, Information Systems

Convolutional neural network with margin loss for fake news detection

Mohammad Hadi Goldani et al.

Summary: This study proposes a method for detecting fake news using Convolutional Neural Networks with different embedding models, comparing static and non-static word embeddings for evaluation on the ISOT and LIAR datasets. The results show promising performance, outperforming existing methods by 7.9% on ISOT and 2.1% on the LIAR dataset.

INFORMATION PROCESSING & MANAGEMENT (2021)

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Article Computer Science, Artificial Intelligence

Multiple features based approach for automatic fake news detection on social networks using deep learning

Somya Ranjan Sahoo et al.

Summary: In recent years, the proliferation of fake news on online social networks has become a serious issue, prompting the need for improved detection and prevention methods. This paper introduces an automatic fake news detection approach based on user profile information and deep learning, which has shown to increase detection accuracy.

APPLIED SOFT COMPUTING (2021)

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Article Computer Science, Information Systems

Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks for Fake News Detection

Shengsheng Qian et al.

Summary: This article focuses on the task of fake news detection and proposes a novel Knowledge-aware Multi-modal Adaptive Graph Convolutional Networks (KMAGCN) model, which jointly models textual information, knowledge concepts, and visual information for effective semantic representation learning in social media posts. Extensive experiments show superior performance in capturing non-consecutive and long-range semantic relations, handling the variability of graph data, and leveraging multiple information sources for model learning.

ACM TRANSACTIONS ON MULTIMEDIA COMPUTING COMMUNICATIONS AND APPLICATIONS (2021)

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Article Computer Science, Information Systems

Entity-Oriented Multi-Modal Alignment and Fusion Network for Fake News Detection

Peiguang Li et al.

Summary: The development of social media allows for fake news to be expressed in a multi-modal form, disseminated on various social platforms, and bring harmful impacts. To detect fake information, a new paradigm of aligning and fusing multi-modal entities was proposed for fake news detection. Our work showed that this entity-centric cross-modal interaction can effectively detect fake news with superior results on public datasets.

IEEE TRANSACTIONS ON MULTIMEDIA (2021)

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Article Computer Science, Information Systems

Trends in combating fake news on social media - a survey

Botambu Collins et al.

Summary: Social media, as a popular and accessible source of information, has been both beneficial and harmful due to the proliferation of misinformation and fake content. The detection and removal of fake news on social media is crucial, and can be effectively addressed through methods such as Natural Language Processing and Hybrid models.

JOURNAL OF INFORMATION AND TELECOMMUNICATION (2021)

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Proceedings Paper Computer Science, Artificial Intelligence