The dynamics of opinion formation in a society is a complex phenomenon where many variables play essential roles. 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 $q_c\left(V\right)$ 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.

社会中意见形成的动态是一个复杂的现象，其中许多变量起着至关重要的作用。近来，用于过滤哪些内容被馈送到社交网络用户的算法的影响已经受到审查。据说，这些算法促进了营销策略，但也可以促进过滤器气泡的形成，在过滤器气泡中，用户最有可能接触到符合他们自己观点的观点。在“两州多数表决”模型中，一个人很可能采用与其多数邻居相反的意见$q$，定义为噪声参数。在这里，我们介绍可见性参数$V$多数表决模型的动态性，等于一个人忽略了每个邻居的意见的可能性。对于$V=0。5$每个人平均将忽略其一半相邻节点的意见。我们采用蒙特卡洛模拟来计算关键噪声参数作为可见度的函数$q_C（V）$并获得模型的相图。我们发现临界噪声是可见性参数的增加函数，因此较低的$V$赞成异议。通过有限尺寸缩放分析，我们获得了模型的关键指数，这些指数与可见性无关，并表明该模型属于Ising通用性类。我们将结果与提交静态站点稀释的网络的情况进行了比较，发现有限的可见性模型是在社交网络中引发观点分化的更微妙的方式。