A Fuzzy Detection System for Rumors through Explainable Adaptive Learning

Zhiwei Guo, Keping Yu, Alireza Jolfaei, Ali Kashif Bashir, Alaa Omran Almagrabi, Neeraj Kumar

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Nowadays, rumor spreading has gradually evolved into a kind of organized behaviors, accompanied with strong uncertainty and fuzziness. However, existing fuzzy detection techniques for rumors focused their attention on supervised scenarios which require expert samples with labels for training. Thus they are not able to well handle unsupervised scenarios where labels are unavailable. To bridge such gap, this paper proposes a fuzzy detection system for rumors through explainable adaptive learning. Specifically, its core is a graph embedding-based generative adversarial network (Graph-GAN) model. First of all, it constructs fine-grained feature spaces via graph-level encoding. Furthermore, it introduces continuous adversarial training between a generator and a discriminator for unsupervised decoding. The two-stage scheme not only solves fuzzy rumor detection under unsupervised scenarios, but also improves robustness of the unsupervised training. Empirically, a set of experiments are carried out based on three real-world datasets. Compared with seven benchmark methods in terms of four metrics, the results of Graph-GAN reveal a proper performance which averagely exceeds baselines by 5% to 10%.

Original languageEnglish
JournalIEEE Transactions on Fuzzy Systems
Publication statusAccepted/In press - 2021


  • Fuzzy detection system
  • cyberspace security
  • generative adversarial learning
  • graph embedding

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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