大数据2026，Vol.12Issue(1)：13-28,16.DOI:10.11959/j.issn.2096-0271.2026013
基于细粒度特征权重专家网络的社交机器人检测方法
Social bot detection method based on fine-grained feature weighted expert network
张怀博 1高金华 2廖逸之 3辛永辉 1程学旗2
作者信息
- 1. 智能算法安全全国重点实验室,北京 100190
- 2. 中国科学院计算技术研究所,北京 100190
- 3. 中国科学院大学,北京 101408
- 折叠
摘要
Abstract
In recent years,research in the field of social bot detection has gradually evolved from individual feature analysis to group feature mining,and from traditional feature engineering to deep learning methods.Among them,graph network-based methods have shown significant advantages:they can integrate account behavior features,text semantic features,and network topology features,converting social bot detection into a graph node classification task.However,most existing detection methods adopt general models for detection and fail to consider the differences in fine-grained features among different types of social bots,which limits the detection accuracy across business scenarios.Based on this,a social bot detection method based on a fine-grained feature expert attention mechanism was proposed.The method constructed a business expert network,enabling each expert to focus on learning differentiated weight combinations of fine-grained features.Through multi-expert feature fusion and comprehensive analysis,it achieved integrated detection of potentially multi-type social bots across various business scenarios.Experimental results on a public Twitter dataset for social bot detection demonstrated that this method outperformed existing mainstream detection methods,with a relative improvement of 1.52% in the F1-score.关键词
社交机器人检测/细粒度特征权重/混合专家网络Key words
social bot detection/fine-grained feature weight/mixture of experts network分类
信息技术与安全科学引用本文复制引用
张怀博,高金华,廖逸之,辛永辉,程学旗..基于细粒度特征权重专家网络的社交机器人检测方法[J].大数据,2026,12(1):13-28,16.
