EAGER: The Identification of Social Systems Trust: Theory and Experimental Validation

社交网络联系人的影响通常被认为比广告或在线声誉/评级系统强得多。有许多理论模型试图预测意见如何在相互信任的个体之间传播，但在真实的环境中定量支持它们的实验证据仍然难以捉摸。该项目旨在缩小意见传播理论与最近对社交媒体数据进行的几项实证研究之间的差距。新颖的想法是利用存在的有影响力的节点，是？固执？，即只信任自己的代理，以确定由于他们的活动以及社交网络中所有代理之间的相对信任，网络中的意见如何波动。从意见扩散的理论模型中获得的数学见解表明，顽固的代理人以这样一种方式激发社会系统，即他们的意见在其他人的评论中的反响可以允许人们为这种现象拟合适当的系统方程。另一个关键的见解是，使用稳态模型是一种更强大的方法来匹配真实的数据，因为意见的波动是不可直接观察的，只有个人的行动和评论。这一努力，如果成功，将类似于实现一个？社交网络雷达，？捕捉社会群体互信的隐藏媒介的断层图像。著名的线性De Groot的意见扩散模型应用于Facebook数据的初步结果显示，从我们的方法中提取的图形与分析组的第一手知识之间具有非常好的一致性。这是一个高风险高回报的项目，因为仍有几个问题需要回答和测试，以验证结果。

The effect of social network contacts is generally believed to be much stronger than either advertising or online reputation/rating systems. There are many theoretical models that try to predict how opinions spread among individuals that trust each other, but the experimental evidence to back them quantitatively in a real setting is still elusive. This project aims to close the gap between the theory of opinion diffusion and the several empirical studies that have been made recently on social media data. The novel idea is to exploit the presence of influential nodes that are ?stubborn?, i.e. agents who trust only themselves, to determine how opinions fluctuate in the network as a result of their activities and the relative trust among all agents in the social network. The mathematical insights from the theoretical models of opinion diffusion indicate that stubborn agents excite the social system in such a way that reverberations of their opinions in comments from others can allow one to fit appropriate system equations to this phenomenon. Another key insight is that using steady state models is a more robust method to match real data, since the fluctuations of opinions are not directly observable, only the individual actions and comments are. This effort, if successful, would be akin to realizing a ?social network Radar,? capturing a tomographic image of the hidden medium of a social group mutual trust. Preliminary results in the case of the celebrated linear De Groot's opinion diffusion model applied to Facebook data show remarkably good agreement between the graph one can extract from our method and first-hand knowledge of the group analyzed. This is a high risk high payoff project as several questions still need to be answered and tested to verify the results.