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 Select Identifying Untrusted Weibo Users Based on Improved Dempster-Shafer Evidence Theory Xu Jianmin, Wang Kailin, Wu Shufang 2022, 6 (12): 99-112.  DOI: 10.11925/infotech.2096-3467.2022.0127 Abstract   HTML ( 5 )  PDF(1641KB) ( 22 )   [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 $F 1$ 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.