Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ 08544;
Informatics Institute, University of Amsterdam,1098XH Amsterdam, The Netherlands.
Proc Natl Acad Sci U S A. 2021 Dec 14;118(50). doi: 10.1073/pnas.2102141118.
The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., "People you may know" or "Whom to follow" suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive-systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity-defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.
近年来，政治团体之间的对抗程度有所上升。某一党派的支持者越来越不喜欢对立党派的成员，并避免群体间的互动，从而导致了同类相吸的社交网络。虽然线下的新联系主要是由人类决策驱动的，但在线社交平台上的新联系是由链接推荐算法中介的，例如“你可能认识的人”或“关注谁”的建议。链接推荐在极化中的长期影响尚不清楚，特别是因为接触对立观点有双重影响：与外群体成员的联系可能导致意见趋同，并防止群体极化或进一步分离意见。在这里，我们从复杂适应系统的角度来看待链接推荐算法的影响。虽然有几个模型通过基于观点相似性的重新布线来证明极化，但在这里，我们通过基于结构相似性的重新布线来解释，结构相似性是基于网络属性的相似性。我们观察到，优先与结构相似的节点（即共享许多邻居）建立联系会导致易于产生意见极化的网络拓扑结构。因此，极化的发生不是因为人们想要屏蔽自己不喜欢的态度，而是因为无意中创建了回音室。当网络由对外部群体接触反应不同的节点组成时，无论是趋同还是极化，我们发现连接结构不同的节点可以缓和意见。总的来说，我们的研究揭示了社会网络算法的影响，并为在线社交网络中的激进化和极化动力学提供了引导途径。
