Multimodal Fusion with Co-Attention Networks for Fake News Detection

By Yang Wu, Pengwei Zhan, Yunjian Zhang, Liming Wang, Zhen Xu. Findings, 2021

376 citations recorded by the source corpus.

Fake news with textual and visual contents has a better story-telling ability than text-only contents, and can be spread quickly with social media. People can be easily deceived by such fake news, and traditional expert identification is labor-intensive. Therefore, automatic detection of multimodal fake news has become a new hot-spot issue. A shortcoming of existing approaches is their inability to fuse multi-modality features effectively. They simply concatenate unimodal features without considering inter-modality relations. Inspired by the way people read news with image and text, we propose a novel Multimodal Co-Attention Networks (MCAN) to better fuse textual and visual features for fake news detection. Extensive experiments conducted on two real-world datasets demonstrate that MCAN can learn inter-dependencies among multimodal features and outperforms state-of-the-art meth-ods.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/a852e1d3adae5179ca75abe1b93b4dcb410cc082

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In Artificial Intelligence

  1. Fei Zhao
  2. Chengcui Zhang
  3. Baocheng Geng

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