A Multi-Reading Habits Fusion Adversarial Network for Multi-Modal Fake News Detection

Existing multimodal fake news detection methods face three challenges: the lack of extraction for implicit shared features, shallow integration of multimodal features, and insufficient at-tention to the inconsistency of features across different modali-ties. To address these challenges, a multi-reading habits fusion adversarial network for multimodal fake news detection is pro-posed. In this model, to mitigate the influence of feature changes due to events and emotions, a dual discriminator based on do-main adversarial training is built to extract invariant common features. Inspired by the diverse reading habits of individuals, three fundamental reading habits are identified, and a multi-reading habits fusion layer is introduced to learn the interde-pendencies among the multimodal feature representations of the news. To investigate the semantic inconsistencies of different modalities in news, a similarity constraint reasoning layer is proposed, which first explores the semantic consistency between image descriptions and unimodal features, and then delves into the semantic discrepancies between unimodal and multimodal features. Extensive experimentation has been carried out on the multimodal datasets of Weibo and Twitter. The outcomes indi-cate that the proposed model surpasses the performance of mainstream advanced benchmarks on both platforms.

@article{Wang2024,
  title     = {A Multi-Reading Habits Fusion Adversarial Network for Multi-Modal Fake News Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Bofan Wang and Shenwu Zhang},
  doi       = {10.14569/IJACSA.2024.0150740},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150740}
}