Robust Few-Label Misinformation Detection Based on Information Bottleneck Theory

Misinformation detection is crucial for the social stability. Researches show that there are substantial distinctions between misinformation and real information in terms of information content and propagation structure. Consequently, recent researchers mainly focus on improving the accuracy of misinformation detection by jointly considering the information content and propagation structure. However, these methods can be infeasible in practice since they highly rely on manual label information. The manual labels can be expensive since they require extensive comparison with official reports and other evidence. Moreover, the spreaders of misinformation can adversarially manipulate the information content and propagation structure by controlling reviews and other methods. Such behaviors may exacerbate the challenges of misinformation detection. To address these problems, we propose a robust few-label misinformation detection method based on information bottleneck theory. Specifically, to mitigate the dependence on labeled data, we propose to integrate the unlabeled sample information by employing the mutual information maximization technique. Furthermore, to improve the robustness of our method against the adversarial manipulation of misinformation spreaders, we employ the adversarial training strategy to simulate the behaviors of the spreaders and propose to learn robust representations based on the information bottleneck theory. The learned representations can effectively embed the essential information in the misinformation while discarding the adversarial information involved by the spreaders. Empirical evaluations validate the effectiveness of the proposed approach, demonstrating superior performance compared with benchmark methods in terms of few-label detection and robustness.