Hierarchical Multi-modal Contextual Attention Network for Fake News Detection

By Shengsheng Qian, Jinguang Wang, Jun Hu, Quan Fang, Changsheng Xu. Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021

298 citations recorded by the source corpus.

Nowadays, detecting fake news on social media platforms has become a top priority since the widespread dissemination of fake news may mislead readers and have negative effects. To date, many algorithms have been proposed to facilitate the detection of fake news from the hand-crafted feature extraction methods to deep learning approaches. However, these methods may suffer from the following limitations: (1) fail to utilize the multi-modal context information and extract high-order complementary information for each news to enhance the detection of fake news; (2) largely ignore the full hierarchical semantics of textual content to assist in learning a better news representation. To overcome these limitations, this paper proposes a novel hierarchical multi-modal contextual attention network (HMCAN) for fake news detection by jointly modeling the multi-modal context information and the hierarchical semantics of text in a unified deep model. Specifically, we employ BERT and ResNet to learn better representations for text and images, respectively. Then, we feed the obtained representations of images and text into a multi-modal contextual attention network to fuse both inter-modality and intra-modality relationships. Finally, we design a hierarchical encoding network to capture the rich hierarchical semantics for fake news detection. Extensive experiments on three public real datasets demonstrate that our proposed HMCAN achieves state-of-the-art performance.

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

View Paper: https://www.semanticscholar.org/paper/1a26f0c35723c6b1fa7be9d3fbb50eb7cf66e6f0

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In Artificial Intelligence

  1. Haoqi Fan
  2. Shengsheng Qian
  3. Yiru Chen
  4. Song Liu
  5. Zhongyuan Wang
  6. Wenkui Ding
  7. Christoph Feichtenhofer
  8. Kaiming He

In Information Systems

  1. Shengsheng Qian
  2. Zhongyuan Wang
  3. Wenkui Ding

In Sociology and Political Science

  1. Shengsheng Qian

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