Fake News Detection in Social Media based on Multi-Modal Multi-Task Learning

The popularity of social media has led to a substantial increase of data. The task of fake news detection is very important, because the authenticity of posts cannot be guaranteed. In recent years, fake news detection combining multimodal information such as images and videos has attracted wide attention from scholars. However, the majority of research work only focuses on the fusion of multi-modal information, while neglecting the role of external evidences. To address this challenge, this paper proposes a fake news detection method based on multi-modal and multi-task learning. When learning the representation of the news posts, this paper models the interaction between images and texts in posts and external evidences through a multi-level attention mechanism, and uses evidence veracity classification as an auxiliary task, so as to improve the task of fake news detection. Authors conduct comprehensive experiments on a public dataset, and demonstrate that the proposed method outperforms several state-of-the-art baselines. The ablation experiment proves the effectiveness of the auxiliary task of evidence veracity in fake news detection.

@article{Cui2022,
  title     = {Fake News Detection in Social Media based on Multi-Modal Multi-Task Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {7},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Xinyu Cui and Yang Li},
  doi       = {10.14569/IJACSA.2022.01307106},
  url       = {https://doi.org/10.14569/IJACSA.2022.01307106}
}