Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 311121, Zhejiang, China.
International Academic Center of Complex Systems, Beijing Normal University, Zhuhai 519087, China.
Chaos. 2023 Nov 1;33(11). doi: 10.1063/5.0172121.
Opinion cascades, initiated by active opinions, offer a valuable avenue for exploring the dynamics of consensus and disagreement formation. Nevertheless, the impact of biased perceptions on opinion cascade, arising from the balance between global information and locally accessible information within network neighborhoods, whether intentionally or unintentionally, has received limited attention. In this study, we introduce a threshold model to simulate the opinion cascade process within social networks. Our findings reveal that consensus emerges only when the collective stubbornness of the population falls below a critical threshold. Additionally, as stubbornness decreases, we observe a higher prevalence of first-order and second-order phase transitions between consensus and disagreement. The emergence of disagreement can be attributed to the formation of echo chambers, which are tightly knit communities where agents' biased perceptions of active opinions are lower than their stubbornness, thus hindering the erosion of active opinions. This research establishes a valuable framework for investigating the relationship between perception bias and opinion formation, providing insights into addressing disagreement in the presence of biased information.
意见级联是由活跃意见引发的，为探索共识和分歧形成的动态提供了有价值的途径。然而，由于网络邻域内全局信息和本地可访问信息之间的平衡，无论是有意还是无意，偏见感知对意见级联的影响受到了有限的关注。在本研究中，我们引入了一个阈值模型来模拟社交网络中的意见级联过程。我们的研究结果表明，只有当群体的集体固执程度低于一个关键阈值时，才会出现共识。此外，随着固执程度的降低，我们观察到在共识和分歧之间存在更高频率的一阶和二阶相变。分歧的出现可以归因于回音室的形成，回音室是紧密结合的社区，其中代理人对活跃意见的偏见感知低于他们的固执程度，从而阻碍了活跃意见的侵蚀。这项研究为研究感知偏差和意见形成之间的关系建立了一个有价值的框架，为在存在偏见信息的情况下解决分歧提供了深入了解。
