EAGER: Collaborative Research: Information Diffusion and Opinion Formation in Networked Systems

在无处不在的通信访问的推动下，网络系统变得无处不在，并产生了依赖于个人决策和网络上的相互作用的行为。此类行为的例子包括媒体分享网站，其中用户的建议影响其他用户的产品采用决策，或者更普遍的公共讨论论坛，其中用户过去的投票记录提供了他们如何相互影响的迹象，因此，最初的意见如何决定未来投票的结果。因此，了解在这种相互关联的环境中决策的演变可能具有重大的社会和经济效益。例如，这可以帮助预测新的社会政策的采用，或者更实际地预测新的共享应用程序的商业成功。这些问题的重要性最近引起了许多关注，但由于网络互动的复杂性，仍有许多工作要做。该项目采用多学科的方法来解决这些具有挑战性的问题，并试图建立统计物理学模型，以捕捉带电粒子的相互作用，这些粒子以类似于用户在社交网络中如何相互影响的方式相互作用。如果成功，这项工作既可以扩展用于探索网络系统行为的工具集，也可以提供对感兴趣的特定问题的见解。本项目探讨了网络系统的两个基本方面，即网络中意见的形成，以及当他们受到网络邻居的影响时，采用决策是如何做出的。网络系统可以有许多不同的形式，包括通信网络、社会网络、政治网络、地理网络等，其特点是网络成员之间的联系会影响他们的相互作用。描述这些相互作用是一项复杂的任务。该项目的两个主要目标是：(i)扩展统计物理模型，将其应用于网络系统中的基本问题；（ii）实证验证这些模型的预测能力。具体来说，该项目旨在利用和扩展Ising自旋玻璃模型，并将这些扩展应用于网络系统中的意见形成和采纳决策问题。然后将通过与从社交媒体网站收集的数据进行比较来寻求结果的实证验证。

Fueled by the ubiquity of communications access, networked systems have become pervasive and given rise to behaviors whose evolution depends on both individual decisions and the interactions on the network. Examples of such behaviors include media sharing websites, where user recommendations influence product adoption decisions of other users, or more generally public discussion forums where past voting records of users provide indications on how they may influence each other and, therefore, how initial opinions may determine the outcome of future votes. Understanding the evolution of decisions in such connected settings can, therefore, be of significant social and economic benefit. For example, this can help predict the adoption of new social policies, or more pragmatically the commercial success of a new shared application. The importance of those questions has attracted much recent attention, but due to the complexity of networked interactions, much remains to be done. This project takes a multi-disciplinary approach to tackling these challenging questions, and seeks to build on models from statistical physics developed to capture the interactions of charged particles, which interact with each other in a manner akin to how users influence each other in a social network. If successful, the work can both expand the set of tools available to explore the behavior of networked systems, and offer insight into specific problems of interest.This project explores two fundamental aspects of networked systems, namely, the formation of opinions in networks, and how adoption decisions are made when they are influenced by network neighbors. Networked systems can be of many different forms, including communication networks, social networks, political networks, geographical networks, etc., and are characterized by the fact that connections between network members influence their interactions. Characterizing these interactions is a complex task. The two main goals of the project are to (i) extend models from statistical physics to apply them to fundamental problems in networked systems; and (ii) empirically validate the predictive abilities of these models. Specifically, the project seeks to leverage and extend the Ising spin glass model, and apply these extensions to problems of opinion formation and adoption decisions in networked systems. Empirical validation of the results will then be sought through comparison to data collected from social media websites.