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

社交网络联系的影响通常被认为比广告或在线声誉/评级系统要强得多。有许多理论模型试图预测观点如何在相互信任的个人之间传播，但在真实环境中定量支持这些观点的实验证据仍然难以找到。该项目旨在弥合意见扩散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。新的思想是利用有影响力的顽固节点的存在，即只信任自己的代理，来确定由于他们的活动以及社会网络中所有代理之间的相对信任，意见在网络中是如何波动的。来自意见扩散理论模型的数学见解表明，顽固的代理人以这样一种方式刺激社会系统，即在他人的评论中对他们的意见进行回响，可以使一个人适合这种现象的适当的系统方程。另一个关键的见解是，使用稳态模型是一种更稳健的方法来匹配真实数据，因为意见的波动不能直接观察到，只有个人的行动和评论才能观察到。这一努力如果成功，将类似于实现一个社交网络雷达。捕捉社会团体互信的隐藏媒介的断层图像。在著名的线性德格鲁特意见扩散模型应用于Facebook数据的情况下，初步结果显示，从我们的方法提取的图表与被分析群体的第一手知识之间有很好的一致性。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.