有限可见度下的多数投票模型：对过滤气泡的研究
Majority-vote model with limited visibility: an investigation into filter bubbles
摘要：
社会舆论形成的动态是一个复杂的现象，许多变量都发挥着重要作用。最近，用于过滤哪些内容被馈送给社交网络用户的算法的影响受到了审查。理论上，这些算法促进了营销策略，但也可以促进过滤气泡的形成，在过滤气泡中，用户最有可能接触到符合他们自己的意见。在两态多数投票模型中，一个个体以概率$q$（定义为噪声参数）采纳与其大多数邻居相反的意见。在这里，我们引入了一个可见性参数$V$的动态的多数票模型，它等于一个人的概率忽略了每一个邻居的意见。对于$V=0.5$，平均而言，每个个体将忽略其相邻节点的一半的意见。我们采用蒙特卡罗模拟计算的临界噪声参数作为可见度$q_c(V)$的函数，并获得该模型的相图。我们发现临界噪声是可见度参数的增函数，因此较低的$V$值有利于不一致。通过有限尺寸标度分析，我们得到了该模型的临界指数，它们是独立的，并表明该模型属于Ising普适类.我们将我们的结果与提交给静态站点稀释的网络的情况进行比较，发现有限可见性模型是在社交网络中诱导意见极化的一种更微妙的方式。
英文摘要：
The dynamics of opinion formation in a society is a complex phenomenon where many variables play an important role. Recently, the influence of algorithms to filter which content is fed to social networks users has come under scrutiny. Supposedly, the algorithms promote marketing strategies, but can also facilitate the formation of filters bubbles in which a user is most likely exposed to opinions that conform to their own. In the two-state majority-vote model an individual adopts an opinion contrary to the majority of its neighbors with probability $q$, defined as the noise parameter. Here, we introduce a visibility parameter $V$ in the dynamics of the majority-vote model, which equals the probability of an individual ignoring the opinion of each one of its neighbors. For $V=0.5$ each individual will, on average, ignore the opinion of half of its neighboring nodes. We employ Monte Carlo simulations to calculate the critical noise parameter as a function of the visibility $q_c(V)$ and obtain the phase diagram of the model. We find that the critical noise is an increasing function of the visibility parameter, such that a lower value of $V$ favors dissensus. Via finite-size scaling analysis we obtain the critical exponents of the model, which are visibility-independent, and show that the model belongs to the Ising universality class. We compare our results to the case of a network submitted to a static site dilution, and find that the limited visibility model is a more subtle way of inducing opinion polarization in a social network.
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