SoCS: A Mathematical Framework for Modeling Behavior of Diverse Groups
SoCS: A Mathematical Framework for Modeling Behavior of Diverse Groups
批准号:
0968370
负责人:
Kristina Lerman
金额:
$25.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2010
资助国家:
美国
项目状态:
已结题
起止时间:
2010-08-01 至 2012-07-31
中文摘要
社交网络已经成为一种重要的社交媒体,并有可能成为一种强大的新计算工具。随着人们在网上创造和分享信息,他们的集体活动塑造了社交网络的结构和有用性,甚至可以用来解决从集体决策到趋势预测的一系列问题。了解许多相互关联的人的聚合活动是如何演变的,对于我们将社交网络转变为社交计算平台的能力至关重要。数学建模是研究人类集体活动的强大工具。在之前的工作中,PI和合作者开发了一个框架,用于对社交网络上用户群体的紧急行为进行数学建模。该框架允许建模师将一组用户的聚合行为与其个人行为的简单描述相关联。然而,它没有考虑到社交网络的关键方面:用户多样性和社交链接表明用户兴趣的共同性的程度。该项目的目标是开发一种方法来对社交网络上不同的用户群体进行建模,并了解用户异质性如何影响群体行为。数学建模和分析将通过确定向用户显示信息的富有成效的方式以及促进合作和加强参与的技术,从而产生更好、更有效的网站。分析还将导致对如何利用人类活动进行计算的新见解,并最终形成用于社会计算的编程工具包。
英文摘要
The Social Web has become an important medium for social interaction and potentially a powerful new computational tool. As people create and share information online, their collective activity shapes the structure and usefulness of the Social Web and can even be used to address a range of problems from collective decision-making to trend prediction. Understanding how the aggregate activity of many interconnected people evolves is crucial to our ability to transform the Social Web into a platform for social computing.Mathematical modeling is a powerful tool for studying collective human activity. In previous work, the PI and collaborators developed a framework for mathematically modeling emergent behavior of groups of users on the Social Web. This framework allowed the modeler to relate aggregate behavior of a group of users to simple descriptions of their individual behavior. However, it failed to take into account key aspects of the Social Web: user diversity and the extent to which social links indicate a commonality of users' interests.The goal of this project is to develop a methodology for modeling diverse groups of users on the Social Web and to understand how user heterogeneity affects group behavior. Mathematical modeling and analysis will lead to better, more effective Web sites by identifying productive ways to display information to users, as well as techniques for promoting collaboration and enhancing participation. Analysis will also lead to new insights into how to use human activity for computation, and eventually a programming toolkit for social computing.
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