Identifying Untrusted Weibo Users Based on Improved Dempster-Shafer Evidence Theory

被引：0
|
作者：
Jianmin X. [1 ]
Kailin W. [1 ]
Shufang W. [2 ]
机构：
[1] College of Cyberspace Security and Computer, Hebei University, Baoding
[2] College of Management, Hebei University, Baoding
来源：
Data Analysis and Knowledge Discovery | 2022年 / 6卷 / 12期
关键词：
Dempster-Shafer Evidence Theory; Microblog; Subjective Uncertainty; Untrusted Users;
D O I：
10.11925/infotech.2096-3467.2022.0127
中图分类号：
学科分类号：
摘要：
[Objective] This paper modifies the Dempster-Shafer evidence theory, aiming to identify untrusted Sina Weibo (Microblog) users with subjective uncertainties. [Methods] Firstly, we used the evidence distance to improve the original Dempster-Shafer evidence theory. Then, we transformed the credibility of historical posts into evidence, which was also merged to generate users’trust interval. Finally, we identified untrusted users with the Decision Tree algorithm and the trust interval. [Results] Compared with the existing methods, our new model reduced the processing time by 287.4 seconds, increased the F1 value by 31.9 percentage point, and received an optimal Chi-Square value of the consistency test. [Limitations] We only investigated the subjective uncertainties due to time decay and evidence conflict, and need to add the impacts of cognitive differences on subjective degrees. [Conclusions] The proposed method could effectively identify untrusted users from Sina Weibo. © 2022, Chinese Academy of Sciences. All rights reserved.
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页码：99 / 112
页数：13
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