Pan Xue, Hou Lei, Liu Kecheng
Informatics Research Centre, Henley Business School, University of Reading, Reading RG6 6UD, United Kingdom.
Data Science and Cloud Service Research Centre, Shanghai University of Finance and Economics, Shanghai 200433, China.
PLoS One. 2017 Apr 13;12(4):e0175761. doi: 10.1371/journal.pone.0175761. eCollection 2017.
Social influence drives human selection behaviours when numerous objects competing for limited attentions, which leads to the 'rich get richer' dynamics where popular objects tend to get more attentions. However, evidences have been found that, both the global information of the whole system and the local information among one's friends have significant influence over the one's selection. Consequently, a key question raises that, it is the local information or the global information more determinative for one's selection? Here we compare the local-based influence and global-based influence. We show that, the selection behaviour is mainly driven by the local popularity of the objects while the global popularity plays a supplementary role driving the behaviour only when there is little local information for the user to refer to. Thereby, we propose a network model to describe the mechanism of user-object interaction evolution with social influence, where the users perform either local-driven or global-driven preferential attachments to the objects, i.e., the probability of an objects to be selected by a target user is proportional to either its local popularity or global popularity. The simulation suggests that, about 75% of the attachments should be driven by the local popularity to reproduce the empirical observations. It means that, at least in the studied context where users chose businesses on Yelp, there is a probability of 75% for a user to make a selection according to the local popularity. The proposed model and the numerical findings may shed some light on the study of social influence and evolving social systems.
当众多对象争夺有限的注意力时，社会影响会驱动人类的选择行为，这会导致“富者愈富”的动态变化，即受欢迎的对象往往会获得更多关注。然而，已经发现证据表明，整个系统的全局信息以及一个人朋友之间的局部信息对这个人的选择都有重大影响。因此，一个关键问题出现了，即对于一个人的选择而言，是局部信息还是全局信息更具决定性？在这里，我们比较基于局部的影响和基于全局的影响。我们表明，选择行为主要由对象的局部受欢迎程度驱动，而全局受欢迎程度仅在用户几乎没有局部信息可参考时才起到补充作用来驱动行为。由此，我们提出一个网络模型来描述具有社会影响的用户 - 对象交互演化机制，其中用户对对象执行局部驱动或全局驱动的优先连接，即目标用户选择某个对象的概率与其局部受欢迎程度或全局受欢迎程度成正比。模拟结果表明，大约75%的连接应由局部受欢迎程度驱动，以重现实证观察结果。这意味着，至少在所研究的用户在Yelp上选择商家的背景下，用户有75%的概率根据局部受欢迎程度进行选择。所提出的模型和数值结果可能会为社会影响和不断演化的社会系统的研究提供一些启示。
