摘要
【目的】解决谣言数据信息量有限和关联常识信息缺乏的问题,提高谣言识别的准确性。【方法】提出一种多分支图卷积推理网络(MGCIN),将双向图卷积网络与常识推理模块相结合,二者通过独立产生分类标签实现共同决策。【结果】在Twitter15、Twitter16和PHEME三个公开数据集上进行实验,结果显示所提模型优于多数基线模型,准确率分别达到87.8%、89.8%和77.6%,并具有优秀的谣言早期检测性能。【局限】谣言数据相关的背景和常识信息的多模态化仍需深入研究。【结论】本文模型能够较好地模拟人类的思维过程,有效融合了文本特征、传播特征和常识信息,为谣言检测研究提供了新的思路和方法。
[Objective]This paper addresses the issues of limited information in rumor data and the lack of associated commonsense information.It improves the accuracy of rumor identification.[Methods]We proposed a Multi-Branch Graph Convolutional Inference Network(MGCIN),which combines a bidirectional graph convolutional network with a commonsense inference module.These two components independently generated classification labels and achieved joint decision-making.[Results]We examined the model on three public datasets:Twitter15,Twitter16,and PHEME.The proposed method outperformed most baseline models,achieving accuracy rates of 87.8%,89.8%,and 77.6%,respectively.It also demonstrated excellent early rumor detection performance.[Limitations]Further research is needed on the multimodality of background and commonsense information related to rumor data.[Conclusions]The proposed model effectively simulates human cognitive processes,successfully integrates textual features,propagation features,and commonsense knowledge,and provides new ideas and methods for rumor detection research.
作者
张益嘉
尹伟鸣
林鸿飞
Zhang Yijia;Yin Weiming;Lin Hongfei(College of Information Science and Technology,Dalian Maritime University,Dalian 116026,China;College of Computer Science and Technology,Dalian University of Technology,Dalian 116023,China)
出处
《数据分析与知识发现》
北大核心
2025年第5期47-61,共15页
Data Analysis and Knowledge Discovery
基金
辽宁省社会科学规划基金项目(项目编号:L20BTQ008)的研究成果之一。
关键词
谣言检测
深度学习
图卷积网络
常识推理
Rumor Detection
Deep Learning
Graph Convolutional Network
Common Sense Inference
