北京大学学报（自然科学版）2026，Vol.62Issue(5)：963-976,14.DOI:10.13209/j.0479-8023.2026.008
基于图补全的多语言社交媒体事件检测
Multi-language Social Media Event Detection Based on Graph Completion
摘要
Abstract
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.关键词
多语言/社交媒体事件检测/属性对齐/随机游走/图补全Key words
multilingual/social media event detection/attribute alignment/random walk/graph completion引用本文复制引用
程效天,马龙轩,张凯,线岩团,余正涛,黄于欣..基于图补全的多语言社交媒体事件检测[J].北京大学学报（自然科学版）,2026,62(5):963-976,14.基金项目
国家自然科学基金(U21B2027,62266028,U23A2038)、云南省重大科技专项(202302AD080003,202402AG050007,202303AP140008)和云南省基础研究(202301AS070047,202301AT070471)资助 （U21B2027,62266028,U23A2038）
