In social sensing environments, misinformation and coordinated manipulation often manifest through implicit semantic signals, complex behavioral dynamics, and highly coupled propagation structures. These factors pose significant challenges to artificial intelligence-driven sensing systems regarding data quality, multimodal fusion, and robustness. To address these issues, this study proposes an artificial intelligence-driven multi-granularity sensing framework. This framework integrates heterogeneous sensing signals from post-level semantic perception, user-level behavioral sensing, and group-level structural sensing into a unified representation space. Hierarchical consistency constraints enable cross-granularity sensing collaboration. This mechanism enhances stability and discriminative capability under complex and noisy data conditions. Methodologically, the framework jointly incorporates semantic sensing via text encoding, temporal sensing via behavioral sequence modeling, and structural sensing via graph neural network-based propagation. This integration effectively mitigates information bias induced by single-perspective sensing and improves the modeling of latent risk patterns. Experimental results on real-world datasets demonstrate that the proposed framework achieves significant improvements across multiple evaluation metrics. Specifically, it achieves a Precision of 0.847, a Recall of 0.812, an F1-score of 0.829, an Accuracy of 0.856, and an Area Under Curve of 0.913. It consistently outperforms traditional machine learning models, as well as mainstream deep learning and graph-based approaches. Furthermore, comparison experiments validate the complementarity among semantic, behavioral, and structural sensing signals. The full model achieves an improvement of more than 3 percentage points in the F1-score compared to single-granularity configurations. An ablation study further demonstrates that each sensing module contributes substantially to performance enhancement, with the semantic sensing and hierarchical consistency constraints playing particularly critical roles. Overall, the proposed method exhibits a strong capability to handle complex heterogeneous sensing data. It improves robustness and enhances cross-level information utilization, providing an effective solution to data-related challenges in artificial intelligence-driven sensing systems.
Chen et al. (Tue,) studied this question.