Abstract: In social networks characterized by frequent and intensive user interactions,disinformation tends to propagate rapidly through collaborative diffusion,exhibiting complex multi-level propagation structures and semantic associations.This represents one of the critical challenges urgently needing to be addressed in the field of national security technology.However,current detection methods,limited to either textual content or conventional propagation graphs,fail to capture these high-order semantic interactions and collaborative behaviors.Therefore,this paper proposes a group-semantics-driven hypergraph network method(GSHN-DD) that integrates propagation structures.The proposed method first constructs an initial hypergraph based on user behaviors and information topics to capture group-level coordination and semantic associations.Subsequently,latent higher-order hyperedges are mined through link prediction combined with a dual-layer filtering mechanism,resulting in an enhanced hypergraph topology.Building on this foundation,a hypergraph convolutional network,combined with a dual-layer attention mechanism,is utilized to integrate global group propagation patterns and local key hyperedge features.Finally,the model integrates propagation features and hypergraph semantic representations to generate unified embeddings,which are fed into a fully connected classifier for disinformation detection.Experimental results on the PolitiFact and GossipCop datasets demonstrate that GSHN-DD performs better than the baseline methods,achieving 2 to 5 percentage point improvement in accuracy and 2 to 7 percentage point increase in F1-score.

Key words: Disinformation detection, Group semantic hypergraph, Link prediction, High-order hyperedge modeling, Hypergraph network