基于图补全的多语言社交媒体事件检测OA

To address the two major issues of reduced embedding similarity due to language differences in multilingual scenarios and the frequent neglect of implicit relational connections in social message graphs,a multilingual social media event detection method called MGCGNN is proposed based on graph completion.First,message embeddings are generated using pre-trained language models,and cross-lingual entity alignment is achieved via Wikipedia entity IDs to construct an initial multilingual social message graph.Subsequently,a random walk strategy is employed to capture long-range semantic associations between nodes,completing the message graph structure and thereby enhancing event clustering effectiveness.Experiments on a trilingual hybrid dataset built upon the Event2012 dataset demonstrate that the proposed method significantly outperforms baseline models,with an average NMI improvement of 4%-5%,validating higher precision and effectiveness in complex multilingual scenarios.