Milgram's experiment in the knowledge space: individual navigation strategies.

IF 3 2区 计算机科学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
EPJ Data Science Pub Date : 2025-01-01 Epub Date: 2025-06-05 DOI:10.1140/epjds/s13688-025-00558-6
Manran Zhu, János Kertész
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引用次数: 0

Abstract

Data deluge characteristic for our times has led to information overload, posing a significant challenge to effectively finding our way through the digital landscape. Addressing this issue requires an in-depth understanding of how we navigate through the abundance of information. Previous research has discovered multiple patterns in how individuals navigate in the geographic, social, and information spaces, yet individual differences in strategies for navigation in the knowledge space has remained largely unexplored. To bridge the gap, we conducted an online experiment where participants played a navigation game on Wikipedia and completed questionnaires about their personal information. Utilizing the hierarchical structure of the English Wikipedia and a graph embedding trained on it, we identified two navigation strategies and found that there are significant individual differences in the choices of them. Older, white and female participants tend to adopt a proximity-driven strategy, while younger participants prefer a hub-driven strategy. Our study connects social navigation to knowledge navigation: individuals' differing tendencies to use geographical and occupational information about the target person to navigate in the social space can be understood as different choices between the hub-driven and proximity-driven strategies in the knowledge space.

米尔格拉姆在知识空间的实验:个人导航策略。
我们这个时代的数据泛滥特征导致了信息超载,对我们在数字环境中有效地找到道路提出了重大挑战。解决这个问题需要深入了解我们如何在丰富的信息中导航。先前的研究已经发现了个体在地理、社会和信息空间中导航的多种模式,但在知识空间中导航策略的个体差异仍未得到很大程度的探索。为了缩小差距,我们进行了一个在线实验,参与者在维基百科上玩一个导航游戏,并完成关于他们个人信息的问卷调查。利用英文维基百科的层次结构和在其上训练的图嵌入,我们确定了两种导航策略,并发现它们的选择存在显著的个体差异。年龄较大的、白人和女性参与者倾向于采用“就近驱动”策略,而年轻的参与者则倾向于采用“中心驱动”策略。我们的研究将社会导航与知识导航联系起来:个体在社会空间中使用目标人的地理和职业信息进行导航的不同倾向可以理解为知识空间中中心驱动策略和邻近驱动策略的不同选择。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
EPJ Data Science
EPJ Data Science MATHEMATICS, INTERDISCIPLINARY APPLICATIONS -
CiteScore
6.10
自引率
5.60%
发文量
53
审稿时长
13 weeks
期刊介绍: EPJ Data Science covers a broad range of research areas and applications and particularly encourages contributions from techno-socio-economic systems, where it comprises those research lines that now regard the digital “tracks” of human beings as first-order objects for scientific investigation. Topics include, but are not limited to, human behavior, social interaction (including animal societies), economic and financial systems, management and business networks, socio-technical infrastructure, health and environmental systems, the science of science, as well as general risk and crisis scenario forecasting up to and including policy advice.
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