A Fuzzy Detection System for Rumors through Explainable Adaptive Learning

Guo, Z, Yu, K, Jolfaei, A, Bashir, AK, Almagrabi, AO and Kumar, N (2021) A Fuzzy Detection System for Rumors through Explainable Adaptive Learning. IEEE Transactions on Fuzzy Systems, 29 (12). pp. 3650-3664. ISSN 1063-6706

Preview

Accepted Version Download (1MB) | Preview

Abstract

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%.