软件导刊2026，Vol.25Issue(8)：23-33,11.DOI:10.11907/rjdk.261203
基于状态条件化动态多模态融合的社交机器人识别方法
State-Conditioned Dynamic Multimodal Fusion for Social Bot Detection
摘要
Abstract
The continuous evolution of social bot accounts on social media platforms has posed severe challenges to cyberspace security,pub-lic opinion governance,and information authenticity.Although existing social bot detection methods have made progress in feature engineer-ing,deep learning,and multimodal fusion,they still suffer from insufficient modality reliability estimation and unstable fusion weight alloca-tion in complex scenarios such as low-activity users,sparse textual information,and incomplete behavioral patterns.To address these prob-lems,this paper proposes a social bot detection method based on state-conditioned dynamic multimodal fusion.The proposed method first ex-tracts users'textual semantic features and behavioral statistical features,respectively,and constructs a user state vector through a learnable state mapping layer to characterize overall state information such as user activity level,information sufficiency,and modality availability.Then,the state information is introduced into the modality weight generation process to achieve dynamic multimodal fusion under state-condi-tioned constraints.Finally,the fused representation is used to complete binary social bot classification.Experimental results on a subset con-structed from TwiBot-22 show that,within the scope of the comparison methods considered in this paper,the proposed method achieves rela-tively superior detection performance.On the low-activity user subset,the proposed method also demonstrates good stability.Ablation experi-ments and parameter sensitivity analysis further verify the effectiveness of the state-conditioned mechanism in improving the adaptive capabili-ty of multimodal fusion.关键词
社交机器人识别/动态多模态学习/状态条件化/特征融合/网络安全Key words
social bot detection/dynamic multimodal learning/state conditioning/feature fusion/cybersecurity分类
信息技术与安全科学引用本文复制引用
刘琦,张晨琳,赵长名..基于状态条件化动态多模态融合的社交机器人识别方法[J].软件导刊,2026,25(8):23-33,11.基金项目
四川省科技计划项目(2023YFG0301) （2023YFG0301）
