@inproceedings{
zhang2024mitigating,
title={Mitigating Social Hazards: Early Detection of Fake News via Diffusion-Guided Propagation Path Generation},
author={Litian Zhang and Xiaoming Zhang and Chaozhuo Li and Ziyi Zhou and Jiacheng Liu and Feiran Huang and Xi Zhang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=olYqZN50xO}
}
The detection of fake news has emerged as a pressing issue in the era of online social media. To detect meticulously fabricated fake news, propagation paths are introduced to provide nuanced social context to complement the pure semantics within news content. However, existing propagation-enhanced models face a dilemma between detection efficacy and social hazard. In this paper, we investigate the novel problem of early fake news detection via propagation path generation, capable of enjoying the merits of rich social context within propagation paths while alleviating potential social hazards. In contrast to previous discriminative detection models, we further propose a novel generative model, DGA-Fake, by simulating realistic propagation paths based on news content before actual spreading. A guided diffusion module is integrated into DGA-Fake to generate simulated user interaction sequences, guided by historical interactions and news content. Evaluation across three datasets demonstrates the superiority of our proposal. Our code is publicly available in https://anonymous.4open.science/r/DGA-Fake-1D5F/.
Mitigating Social Hazards: Early Detection of Fake News via Diffusion-Guided Propagation Path Generation
Litian Zhang Xiaoming Zhang Chaozhuo Li Ziyi Zhou Jiacheng Liu Feiran Huang Xi Zhang