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

doi:10.11925/infotech.2096-3467.2022.0127

Data Analysis and Knowledge Discovery

2022, Vol. 6

Issue (12): 99-112 DOI: 10.11925/infotech.2096-3467.2022.0127

Current Issue | Archive | Adv Search

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

Xu Jianmin¹,Wang Kailin¹,Wu Shufang²(

)

¹College of Cyberspace Security and Computer, Hebei University, Baoding 071002, China
²College of Management, Hebei University, Baoding 071002, China

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Abstract

[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.

Key words： Microblog Untrusted Users Subjective Uncertainty Dempster-Shafer Evidence Theory

Received: 17 February 2022 Published: 03 February 2023

ZTFLH:	G203
	TP182

Fund:National Social Science Fund of China(17BTQ068);Key Projects of Humanities and Social Sciences in Hebei Province(ZD202102)

Corresponding Authors: Wu Shufang，ORCID：0000-0002-9885-6944 E-mail: shufang_44@126.com

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Cite this article:

Xu Jianmin, Wang Kailin, Wu Shufang. Identifying Untrusted Weibo Users Based on Improved Dempster-Shafer Evidence Theory. Data Analysis and Knowledge Discovery, 2022, 6(12): 99-112.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.0127 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I12/99

A Framework for Untrusted Users Identification on Microblog Based on Improved Dempster-Shafer Theory of Evidence

Example of Untrusted Posts

A Fragment of the Emoji Mapping Table

Example of Emoji Conversion

	$A 0 = {θ 0}$	$A 1 = {θ 1}$	$A 2 = {θ 0, θ 1}$
m₁	1.0	0.0	0.0
m₂	0.0	1.0	0.0
M_ideal	0.0	0.0	1.0
M	不存在	不存在	不存在

Example of Conflict Evidence Integration

命题A_j	含义
$A 0 =$ {用户可信}	微博用户可信
$A 1 =$ {用户不可信}	微博用户不可信
$A 2 =$ {用户可信，用户不可信}	无法判断微博用户的可信性
$A 3 = ?$	未进行可信性判断

Propositions on Frame

Θ

Sample Distribution of Untrusted Users

Introduction to Comparison Methods

	信用极低	信用较低	信用一般	信用较好	信用极好	总计
识别不可信数	$u 0$	$u 1$	$u 2$	$u 3$	$u 4$	$∑ u$
实际不可信数	$v 0$	$v 1$	$v 2$	$v 3$	$v 4$	$∑ v$
总计	$u 0 + v 0$	$u 1 + v 1$	$u 2 + v 2$	$u 3 + v 3$	$u 4 + v 4$	$∑ u + ∑ v$

The Contingency Table About Credit Ratings and Credibility of Users

Time Consumption on 5 Test Sets

F1-Measure on Fraudulent， Pornographic， Insulting， Script-based Users

Recall on Fraudulent， Pornographic， Insulting， Script-based Users

Precision on Fraudulent， Pornographic， Insulting， Script-based Users

识别方法	$F 1$ 值	召回率	精确率
DS-DT	0.812	0.738	0.902
E-N	0.679	0.665	0.693
DDTLS	0.804	0.716	0.917
Truser	0.608	0.531	0.711
A-D	0.493	0.408	0.622

F1-measure， Recall and Precision on Total Users

方法	$χ 2$
DS-DT	533.65
E-N	688.15
DDTLS	563.46
Truser	579.21
A-D	756.87

Chi-Square Value

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