Humanity for centuries has perfected skills of interpersonal interactions and evolved patterns that enable people to detect lies and deceiving behavior of others in face-to-face settings. Unprecedented growth of people's access to mobile phones and social media raises an important question: How does this new technology influence people's interactions and support the use of traditional patterns? In this article, we answer this question for homophily-driven patterns in social media. In our previous studies, we found that, on a university campus, changes in student opinions were driven by the desire to hold popular opinions. Here, we demonstrate that the evolution of online platform-wide opinion groups is driven by the same desire. We focus on two social media: Twitter and Parler, on which we tracked the political biases of their users. On Parler, an initially stable group of Right-biased users evolved into a permanent Right-leaning echo chamber dominating weaker, transient groups of members with opposing political biases. In contrast, on Twitter, the initial presence of two large opposing bias groups led to the evolution of a bimodal bias distribution, with a high degree of polarization. We capture the movement of users from the initial to final bias groups during the tracking period. We also show that user choices are influenced by side-effects of homophily. Users entering the platform attempt to find a sufficiently large group whose members hold political biases within the range sufficiently close to their own. If successful, they stabilize their biases and become permanent members of the group. Otherwise, they leave the platform. We believe that the dynamics of users' behavior uncovered in this article create a foundation for technical solutions supporting social groups on social media and socially aware networks.

中文翻译：

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社交媒体用户意识形态偏见的动态

几个世纪以来，人类已经完善了人际交往的技能，并进化了模式，使人们能够在面对面的环境中察觉他人的谎言和欺骗行为。人们使用手机和社交媒体的空前增长提出了一个重要问题：这项新技术如何影响人们的互动并支持传统模式的使用？在本文中，我们针对社交媒体中同质驱动的模式回答了这个问题。在我们之前的研究中，我们发现，在大学校园里，学生观点的变化是由持有流行观点的愿望驱动的。在这里，我们证明了在线平台范围内意见团体的演变是由相同的愿望驱动的。我们关注两个社交媒体：Twitter 和 Parler，我们在这两个媒体上跟踪其用户的政治偏见。在帕勒上，最初稳定的右翼用户群体演变成一个永久性的右倾回声室，主导着具有相反政治偏见的较弱、短暂的成员群体。相比之下，在 Twitter 上，两个大的对立偏见群体最初的存在导致了双峰偏见分布的演变，并且具有高度的两极分化。我们捕获跟踪期间用户从初始偏见组到最终偏见组的移动情况。我们还表明，用户选择会受到同质性副作用的影响。进入平台的用户试图找到一个足够大的群体，其成员的政治偏见与自己的政治偏见足够接近。如果成功，他们就会稳定自己的偏见并成为该群体的永久成员。否则，他们将离开平台。我们相信，本文中揭示的用户行为动态为支持社交媒体和社交意识网络上的社交群体的技术解决方案奠定了基础。