Link structures, information flow, and social processes

J. Kleinberg
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Abstract

In the ongoing movement toward socially-produced information resources, we are increasingly able to look through the content being created and see the individuals, incentives, and larger social processes at work. Designing and working with large-scale participatory social computing applications requires that we think not just about technological issues, but also about fundamental principles of human social interaction. Through the digital traces that these applications generate, we can begin to quantify and reason about such principles at unprecedented levels of scale and resolution. In this talk, we consider a crucial type of social process in this setting - the mechanisms by which information flows through groups of people engaged in sharing and synthesizing knowledge. As information, ideas, opinions, and beliefs spread through an underlying social network, their dynamics resemble that of an epidemic, moving "contagiously" from person to person. But social contagion is different from biological contagion in many respects; understanding the analogies and contrasts between these two kinds of processes leads us to consider the rich temporal characteristics of information flow within a network and the complex decision rules by which people choose to act on new information. The result is a richer picture of the communities that create knowledge and its interlinkages, and of the resources that ultimately arise from these processes.
链接结构、信息流和社会过程
在社会生产信息资源的持续运动中,我们越来越能够通过正在创建的内容,看到个人、动机和更大的社会过程在起作用。设计和使用大规模的参与式社会计算应用程序要求我们不仅要考虑技术问题,还要考虑人类社会互动的基本原则。通过这些应用程序产生的数字痕迹,我们可以开始以前所未有的规模和分辨率量化和推理这些原则。在这次演讲中,我们将考虑在这种情况下的一种关键类型的社会过程——信息在参与分享和综合知识的人群中流动的机制。随着信息、思想、观点和信仰通过潜在的社会网络传播,它们的动态类似于流行病,在人与人之间“传染”。但社会传染在许多方面不同于生物传染;理解这两种过程之间的类比和对比,使我们考虑到网络中信息流的丰富的时间特征,以及人们选择对新信息采取行动的复杂决策规则。其结果是对创造知识及其相互联系的社区以及最终从这些过程中产生的资源有了更丰富的了解。
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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