Evolution of Users' Knowledge Sharing and Hiding Behaviors in Online Health Community

doi:10.11925/infotech.2096-3467.2022.1030

Data Analysis and Knowledge Discovery

2023, Vol. 7

Issue (11): 125-139 DOI: 10.11925/infotech.2096-3467.2022.1030

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Evolution of Users' Knowledge Sharing and Hiding Behaviors in Online Health Community

Huang Zixuan(

),Xiong Huixiang

School of Information Management, Central China Normal University, Wuhan 430079, China

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Abstract

[Objective] This paper studies the decision-making rules for knowledge sharing and hiding among users of online health communities. It tries to improve the users’ overall health knowledge levels. [Methods] First, we constructed an evolutionary game model for the decision-making mechanism of lurkers and sharers’ knowledge-sharing and hiding behaviors. Then, we retrieved data on breast cancer topics from the Zhihu platform to assign model parameters. Finally, we conducted numerical experiments with Matlab to explore the impacts of parameter changes. [Results] The transformation of users from knowledge hiding to sharing was positively affected by the benefits of knowledge innovation, emotion, and community rewards. Privacy risks and coding costs posed negative impacts on user behaviors. The two user groups had different sensitivity levels to the factors. [Limitations] We did not set a nonlinear utility function; manually labeling data may yield errors. [Conclusions] This paper could help the online health community transform users from knowledge hiding to sharing.

Key words： Online Health Community Knowledge Sharing Knowledge Hiding Evolutionary Game User Behavior

Received: 28 September 2022 Published: 22 March 2023

ZTFLH:

G252 R-05

Fund:National Social Science Fund of China(22ATQ004);Cross-scientific Research Project of the Basic Research Funds of Central China Normal University in 2022(CCNU22JC033)

Corresponding Authors: Huang Zixuan，ORCID：0000-0002-7262-2883，E-mail：huangzx85@mail2.sysu.edu.cn。

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Cite this article:

Huang Zixuan, Xiong Huixiang. Evolution of Users' Knowledge Sharing and Hiding Behaviors in Online Health Community. Data Analysis and Knowledge Discovery, 2023, 7(11): 125-139.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1030 OR https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I11/125

符号	参数解释	取值范围及条件
$I m 1, I m 2$	用户提供的信息支持	$0 ≤ I m 2 < I m 1, 0 < I i 1 < I j 1, 0 ≤ I i 2 < I j 2$
$E m 1, E m 2$	用户获得的情感支持	$0 ≤ E m 2 < E m 1, 0 < E i 1 < E j 1, 0 ≤ E i 2 < E j 2$
$M$	社区对用户发布一次健康知识的单位奖励	$M ≥ 0$
$μ m$	社区奖励对用户的实际激励效果	$μ m ≥ 0$
$p m 1, p m 2$	用户感知的隐私风险	$0 ≤ p m 2 < p m 1, 0 < p i 1 < p j 1, 0 ≤ p i 2 < p j 2$
$r$	知识编码所需的时间和精力	$0 < r < 1$
$O$	用户对社区系统进行访问、操作而产生的成本	$O > 0$
$e$	两类用户间的知识相似程度（重复率）	$0 < e < 1$
$h$	知识创新系数，代表产生新知识的能力	$0 < h < 1$
$β$	社区信任，用户对社区制度、技术、服务的安全性、可靠性、易用性的信任程度	$0 < β < 1$
$t$	人际信任，用户对其他成员积极互惠的信任程度	$0 < t < 1$

Summary of Model Parameters

博弈得益矩阵		共享者
博弈得益矩阵		知识共享（ $y$ ）	知识隐藏（ $1 - y$ ）
潜水者	知识共享（ $x$ ）	$a 1, b 1$	$a 2, b 2$
潜水者	知识隐藏（ $1 - x$ ）	$a 3, b 3$	$a 4, b 4$

Evolutionary Game Benefit Matrix

情景	条件	局部平衡点
情景	条件	$D 1 0,0$	$D 2 0,1$	$D 3 1,0$	$D 4 1,1$	$D 5 (x , y )$
情景1	$d e t (J)$	+	-	-	+	-
	$t r (J)$	+	不定	不定	-	0
	稳定性	不稳定	鞍点	鞍点	ESS	鞍点
情景2	$d e t (J)$	-	+	+	-	-
	$t r (J)$	不定	+	-	不定	0
	稳定性	鞍点	不稳定	ESS	鞍点	鞍点
情景3	$d e t (J)$	-	+	+	-	+
	$t r (J)$	不定	-	+	不定	0
	稳定性	鞍点	ESS	不稳定	鞍点	鞍点
情景4	$d e t (J)$	+	-	-	+	-
	$t r (J)$	-	不定	不定	+	0
	稳定性	ESS	鞍点	鞍点	不稳定	鞍点
情景5	$d e t (J)$	+	+	+	+	-
	$t r (J)$	-	+	+	-	0
	稳定性	ESS	不稳定	不稳定	ESS	鞍点

Local Equilibrium Stability Analysis Results

Distribution of Lurkers and Sharers

Statistics of Users’ Behavior Strategy

Process of Manual Labeling

Classification Statistics of Direct Information Benefits， Emotion Benefits， and Privacy Risks

情景	其余参数
情景	$μ i$	$μ j$	$M$	$r$	$β$	$t$	$h$	$e$
情景1	1.0	1.0	0.1	0.30	0.5	0.5	0.4	0.5
情景2	2.5	0.0	0.5	0.50	0.2	0.2	0.3	0.6
情景3	0.0	1.5	0.5	0.50	0.2	0.2	0.3	0.7
情景4	1.0	1.0	0.1	0.30	0.5	0.5	0.4	0.6
情景5	1.0	1.0	0.1	0.25	0.5	0.5	0.4	0.6

Parameters of the Model Under the Five Scenarios

Evolution Process of Users’ Behavior Strategies Under the Five Scenarios

Sensitivity Analysis of Direct Information Benefits

Sensitivity Analysis of Knowledge Innovation Benefits

Sensitivity Analysis of Emotion Benefits

M = 0.1）
">

Sensitivity Analysis of Perceived Reward Utility Coefficient （ $M = 0.1$ ）

M = 0.2）
">

Sensitivity Analysis of Perceived Reward Utility Coefficient （ $M = 0.2$ ）

Sensitivity Analysis of Privacy Risks

Sensitivity Analysis of Community Trust

Sensitivity Analysis of Interpersonal Trust

Sensitivity Analysis of Coding Costs Coefficient

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