多模态数据驱动的社交网络谣言传播者识别方法研究  

ldentifying Social Network Rumor Spreaders with Multi-Modal Data

作　　者：潘宏鹏 刘忠轶 Pan Hongpeng;Liu Zhongyi(School of Management,People's Public Security University of China,Beijing100038,China)

机构地区：[1]中国人民公安大学公安管理学院,北京100038

出　　处：《数据分析与知识发现》2025年第2期59-70,共12页Data Analysis and Knowledge Discovery

基　　金：中国人民公安大学基本科研业务费项目(项目编号:2022JKF02004);北京社会科学基金重点项目(项目编号:22GLA011)的研究成果之一。

摘　　要：【目的】根据多模态数据特征,从社交网络用户中识别出社交网络谣言传播者。【方法】考虑到网络谣言传播呈现多模态与用户样本不平衡的特点,首先对原始数据进行过采样处理,然后将用户属性、微博发文等传统特征与用户生成内容中的多模态信息特征深度融合;在XGBoost模型基础上构建能够广泛融合社交网络用户特征的社交网络谣言传播者识别框架,并在模型输出层嵌入SHAP值,增加算法可解释性。【结果】XGBoost模型在经过样本平衡处理的数据集上综合性能表现最优,召回率提升12.3个百分点。融合多模态信息特征的识别方法准确率可达0.912,比对照组提升2.5个百分点。【局限】多模态信息特征仅考虑文本、图片两种模态,未来可进一步结合音频、视频等模态信息拓展研究。【结论】基于多模态数据与过采样算法训练得到的识别方法,能够有效完成社交网络谣言传播者的识别任务。[Objective]This paper aims to identify social network rumor spreaders by leveraging multi-modal data.[Methods]Given the multi-modal nature of rumor propagation and the imbalance in user sample distribution,we first applied an oversampling technique to the raw data.Then,we deeply integrated traditional user attributes and microblogging features with multi-modal information extracted from user-generated content.Third,we constructed the intelligent identification method for social network rumor spreaders,which effectively integrates diverse user features based on the XGBoost model.Additionally,SHAP values were embedded in the model's output layer to enhance algorithmic interpretability.[Results]The XGBoost model achieves optimal overall performance after sample balancing,with a 12.3%improvement in recall.The identification method incorporating multi-modal information features can attain an accuracy of 0.912,2.5%higher than the control group.[Limitations]This paper only considered text and image modalities.Future research can be expanded by incorporating audio and video data.[Conclusions]The proposed model can effectively identify social network rumor spreaders.

关 键 词：多模态特征 过采样 网络谣言传播者 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]